Overview

Brought to you by YData

Dataset statistics

Number of variables81
Number of observations520307
Missing cells608930
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 GiB
Average record size in memory3.5 KiB

Variable types

Categorical52
Text7
Numeric22

Alerts

ESTU_ESTUDIANTE has constant value "ESTUDIANTE"Constant
COLE_AREA_UBICACION is highly overall correlated with COLE_COD_DANE_ESTABLECIMIENTO and 1 other fieldsHigh correlation
COLE_CALENDARIO is highly overall correlated with PERIODOHigh correlation
COLE_COD_DANE_ESTABLECIMIENTO is highly overall correlated with COLE_AREA_UBICACION and 2 other fieldsHigh correlation
COLE_COD_DANE_SEDE is highly overall correlated with COLE_AREA_UBICACION and 2 other fieldsHigh correlation
COLE_COD_DEPTO_UBICACION is highly overall correlated with COLE_COD_MCPIO_UBICACION and 7 other fieldsHigh correlation
COLE_COD_MCPIO_UBICACION is highly overall correlated with COLE_COD_DEPTO_UBICACION and 7 other fieldsHigh correlation
COLE_DEPTO_UBICACION is highly overall correlated with COLE_COD_DEPTO_UBICACION and 6 other fieldsHigh correlation
COLE_JORNADA is highly overall correlated with COLE_NATURALEZAHigh correlation
COLE_NATURALEZA is highly overall correlated with COLE_COD_DANE_ESTABLECIMIENTO and 3 other fieldsHigh correlation
DESEMP_C_NATURALES is highly overall correlated with PERCENTIL_C_NATURALES and 3 other fieldsHigh correlation
DESEMP_INGLES is highly overall correlated with PERCENTIL_INGLES and 1 other fieldsHigh correlation
DESEMP_LECTURA_CRITICA is highly overall correlated with PERCENTIL_GLOBAL and 3 other fieldsHigh correlation
DESEMP_MATEMATICAS is highly overall correlated with PERCENTIL_GLOBAL and 3 other fieldsHigh correlation
DESEMP_SOCIALES_CIUDADANAS is highly overall correlated with PERCENTIL_GLOBAL and 5 other fieldsHigh correlation
ESTU_COD_DEPTO_PRESENTACION is highly overall correlated with COLE_COD_DEPTO_UBICACION and 7 other fieldsHigh correlation
ESTU_COD_MCPIO_PRESENTACION is highly overall correlated with COLE_COD_DEPTO_UBICACION and 7 other fieldsHigh correlation
ESTU_COD_RESIDE_DEPTO is highly overall correlated with COLE_COD_DEPTO_UBICACION and 5 other fieldsHigh correlation
ESTU_COD_RESIDE_MCPIO is highly overall correlated with COLE_COD_DEPTO_UBICACION and 7 other fieldsHigh correlation
ESTU_DEDICACIONINTERNET is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
ESTU_DEDICACIONLECTURADIARIA is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
ESTU_DEPTO_PRESENTACION is highly overall correlated with COLE_COD_DEPTO_UBICACION and 6 other fieldsHigh correlation
ESTU_DEPTO_RESIDE is highly overall correlated with COLE_COD_DEPTO_UBICACION and 7 other fieldsHigh correlation
ESTU_HORASSEMANATRABAJA is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
ESTU_INSE_INDIVIDUAL is highly overall correlated with ESTU_NSE_INDIVIDUAL and 7 other fieldsHigh correlation
ESTU_NACIONALIDAD is highly overall correlated with ESTU_PAIS_RESIDEHigh correlation
ESTU_NSE_ESTABLECIMIENTO is highly overall correlated with COLE_NATURALEZA and 1 other fieldsHigh correlation
ESTU_NSE_INDIVIDUAL is highly overall correlated with ESTU_INSE_INDIVIDUAL and 5 other fieldsHigh correlation
ESTU_PAIS_RESIDE is highly overall correlated with ESTU_NACIONALIDADHigh correlation
ESTU_PRIVADO_LIBERTAD is highly overall correlated with ESTU_DEDICACIONINTERNET and 25 other fieldsHigh correlation
ESTU_TIPOREMUNERACION is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_COMECARNEPESCADOHUEVO is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_COMECEREALFRUTOSLEGUMBRE is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_COMELECHEDERIVADOS is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_CUARTOSHOGAR is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_EDUCACIONMADRE is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_EDUCACIONPADRE is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_ESTRATOVIVIENDA is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_NUMLIBROS is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_PERSONASHOGAR is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_SITUACIONECONOMICA is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_TIENEAUTOMOVIL is highly overall correlated with ESTU_INSE_INDIVIDUAL and 1 other fieldsHigh correlation
FAMI_TIENECOMPUTADOR is highly overall correlated with ESTU_INSE_INDIVIDUAL and 3 other fieldsHigh correlation
FAMI_TIENECONSOLAVIDEOJUEGOS is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_TIENEHORNOMICROOGAS is highly overall correlated with ESTU_INSE_INDIVIDUAL and 2 other fieldsHigh correlation
FAMI_TIENEINTERNET is highly overall correlated with ESTU_INSE_INDIVIDUAL and 3 other fieldsHigh correlation
FAMI_TIENELAVADORA is highly overall correlated with ESTU_INSE_INDIVIDUAL and 1 other fieldsHigh correlation
FAMI_TIENEMOTOCICLETA is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_TIENESERVICIOTV is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_TRABAJOLABORMADRE is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
FAMI_TRABAJOLABORPADRE is highly overall correlated with ESTU_PRIVADO_LIBERTADHigh correlation
PERCENTIL_C_NATURALES is highly overall correlated with DESEMP_C_NATURALES and 11 other fieldsHigh correlation
PERCENTIL_GLOBAL is highly overall correlated with DESEMP_C_NATURALES and 14 other fieldsHigh correlation
PERCENTIL_INGLES is highly overall correlated with DESEMP_INGLES and 11 other fieldsHigh correlation
PERCENTIL_LECTURA_CRITICA is highly overall correlated with DESEMP_LECTURA_CRITICA and 12 other fieldsHigh correlation
PERCENTIL_MATEMATICAS is highly overall correlated with DESEMP_MATEMATICAS and 11 other fieldsHigh correlation
PERCENTIL_SOCIALES_CIUDADANAS is highly overall correlated with DESEMP_SOCIALES_CIUDADANAS and 11 other fieldsHigh correlation
PERIODO is highly overall correlated with COLE_CALENDARIO and 3 other fieldsHigh correlation
PUNT_C_NATURALES is highly overall correlated with DESEMP_C_NATURALES and 12 other fieldsHigh correlation
PUNT_GLOBAL is highly overall correlated with DESEMP_C_NATURALES and 14 other fieldsHigh correlation
PUNT_INGLES is highly overall correlated with DESEMP_INGLES and 11 other fieldsHigh correlation
PUNT_LECTURA_CRITICA is highly overall correlated with DESEMP_LECTURA_CRITICA and 11 other fieldsHigh correlation
PUNT_MATEMATICAS is highly overall correlated with DESEMP_MATEMATICAS and 11 other fieldsHigh correlation
PUNT_SOCIALES_CIUDADANAS is highly overall correlated with DESEMP_SOCIALES_CIUDADANAS and 11 other fieldsHigh correlation
ESTU_TIPODOCUMENTO is highly imbalanced (78.1%)Imbalance
ESTU_NACIONALIDAD is highly imbalanced (98.9%)Imbalance
PERIODO is highly imbalanced (80.7%)Imbalance
ESTU_PAIS_RESIDE is highly imbalanced (98.9%)Imbalance
ESTU_TIENEETNIA is highly imbalanced (66.4%)Imbalance
COLE_GENERO is highly imbalanced (84.4%)Imbalance
COLE_CALENDARIO is highly imbalanced (87.3%)Imbalance
COLE_BILINGUE is highly imbalanced (86.4%)Imbalance
COLE_SEDE_PRINCIPAL is highly imbalanced (73.0%)Imbalance
ESTU_PRIVADO_LIBERTAD is highly imbalanced (99.6%)Imbalance
ESTU_ESTADOINVESTIGACION is highly imbalanced (99.4%)Imbalance
FAMI_ESTRATOVIVIENDA has 18510 (3.6%) missing valuesMissing
FAMI_PERSONASHOGAR has 16580 (3.2%) missing valuesMissing
FAMI_CUARTOSHOGAR has 17278 (3.3%) missing valuesMissing
FAMI_EDUCACIONPADRE has 14539 (2.8%) missing valuesMissing
FAMI_EDUCACIONMADRE has 15030 (2.9%) missing valuesMissing
FAMI_TRABAJOLABORPADRE has 21147 (4.1%) missing valuesMissing
FAMI_TRABAJOLABORMADRE has 19059 (3.7%) missing valuesMissing
FAMI_TIENEINTERNET has 15194 (2.9%) missing valuesMissing
FAMI_TIENESERVICIOTV has 16983 (3.3%) missing valuesMissing
FAMI_TIENECOMPUTADOR has 21616 (4.2%) missing valuesMissing
FAMI_TIENELAVADORA has 17659 (3.4%) missing valuesMissing
FAMI_TIENEHORNOMICROOGAS has 18579 (3.6%) missing valuesMissing
FAMI_TIENEAUTOMOVIL has 19264 (3.7%) missing valuesMissing
FAMI_TIENEMOTOCICLETA has 18537 (3.6%) missing valuesMissing
FAMI_TIENECONSOLAVIDEOJUEGOS has 18929 (3.6%) missing valuesMissing
FAMI_NUMLIBROS has 15606 (3.0%) missing valuesMissing
FAMI_COMELECHEDERIVADOS has 15265 (2.9%) missing valuesMissing
FAMI_COMECARNEPESCADOHUEVO has 16841 (3.2%) missing valuesMissing
FAMI_COMECEREALFRUTOSLEGUMBRE has 21811 (4.2%) missing valuesMissing
FAMI_SITUACIONECONOMICA has 17764 (3.4%) missing valuesMissing
ESTU_DEDICACIONLECTURADIARIA has 16013 (3.1%) missing valuesMissing
ESTU_DEDICACIONINTERNET has 16179 (3.1%) missing valuesMissing
ESTU_HORASSEMANATRABAJA has 18404 (3.5%) missing valuesMissing
ESTU_TIPOREMUNERACION has 19104 (3.7%) missing valuesMissing
COLE_BILINGUE has 85622 (16.5%) missing valuesMissing
COLE_CARACTER has 15105 (2.9%) missing valuesMissing
ESTU_INSE_INDIVIDUAL has 29380 (5.6%) missing valuesMissing
ESTU_NSE_INDIVIDUAL has 29380 (5.6%) missing valuesMissing
ESTU_NSE_ESTABLECIMIENTO has 15484 (3.0%) missing valuesMissing
ESTU_COD_RESIDE_DEPTO is highly skewed (γ1 = 164.8753971)Skewed
ESTU_CONSECUTIVO has unique valuesUnique

Reproduction

Analysis started2024-10-24 09:41:47.578535
Analysis finished2024-10-24 09:44:58.918339
Duration3 minutes and 11.34 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

ESTU_TIPODOCUMENTO
Categorical

IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.3 MiB
TI
426020 
CC
86101 
CR
 
4985
NES
 
1466
CE
 
1177
Other values (7)
 
558

Length

Max length4
Median length2
Mean length2.0036786
Min length1

Characters and Unicode

Total characters1042528
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCC
2nd rowCC
3rd rowCC
4th rowCC
5th rowCC

Common Values

ValueCountFrequency (%)
TI 426020
81.9%
CC 86101
 
16.5%
CR 4985
 
1.0%
NES 1466
 
0.3%
CE 1177
 
0.2%
PEP 445
 
0.1%
PE 77
 
< 0.1%
RC 27
 
< 0.1%
PC 4
 
< 0.1%
V 2
 
< 0.1%
Other values (2) 3
 
< 0.1%

Length

2024-10-24T11:44:58.965012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ti 426020
81.9%
cc 86101
 
16.5%
cr 4985
 
1.0%
nes 1466
 
0.3%
ce 1177
 
0.2%
pep 445
 
0.1%
pe 77
 
< 0.1%
rc 27
 
< 0.1%
pc 4
 
< 0.1%
v 2
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 426023
40.9%
T 426020
40.9%
C 178395
17.1%
R 5012
 
0.5%
E 3165
 
0.3%
N 1469
 
0.1%
S 1466
 
0.1%
P 974
 
0.1%
V 2
 
< 0.1%
U 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1042528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 426023
40.9%
T 426020
40.9%
C 178395
17.1%
R 5012
 
0.5%
E 3165
 
0.3%
N 1469
 
0.1%
S 1466
 
0.1%
P 974
 
0.1%
V 2
 
< 0.1%
U 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1042528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 426023
40.9%
T 426020
40.9%
C 178395
17.1%
R 5012
 
0.5%
E 3165
 
0.3%
N 1469
 
0.1%
S 1466
 
0.1%
P 974
 
0.1%
V 2
 
< 0.1%
U 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1042528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 426023
40.9%
T 426020
40.9%
C 178395
17.1%
R 5012
 
0.5%
E 3165
 
0.3%
N 1469
 
0.1%
S 1466
 
0.1%
P 974
 
0.1%
V 2
 
< 0.1%
U 2
 
< 0.1%

ESTU_NACIONALIDAD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.3 MiB
COLOMBIA
517029 
VENEZUELA
 
2975
ESTADOS UNIDOS
 
62
ECUADOR
 
45
ESPAÑA
 
41
Other values (43)
 
155

Length

Max length31
Median length8
Mean length8.0061733
Min length4

Characters and Unicode

Total characters4165668
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA 517029
99.4%
VENEZUELA 2975
 
0.6%
ESTADOS UNIDOS 62
 
< 0.1%
ECUADOR 45
 
< 0.1%
ESPAÑA 41
 
< 0.1%
ARGENTINA 14
 
< 0.1%
CUBA 10
 
< 0.1%
MÉXICO 10
 
< 0.1%
BRASIL 10
 
< 0.1%
PERÚ 9
 
< 0.1%
Other values (38) 102
 
< 0.1%

Length

2024-10-24T11:44:59.022402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colombia 517029
99.3%
venezuela 2975
 
0.6%
estados 63
 
< 0.1%
unidos 62
 
< 0.1%
ecuador 45
 
< 0.1%
españa 41
 
< 0.1%
argentina 14
 
< 0.1%
cuba 10
 
< 0.1%
méxico 10
 
< 0.1%
brasil 10
 
< 0.1%
Other values (54) 153
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 1034285
24.8%
A 520421
12.5%
L 520061
12.5%
I 517198
12.4%
C 517141
12.4%
B 517063
12.4%
M 517061
12.4%
E 9145
 
0.2%
U 3149
 
0.1%
N 3121
 
0.1%
Other values (23) 7023
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4165668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1034285
24.8%
A 520421
12.5%
L 520061
12.5%
I 517198
12.4%
C 517141
12.4%
B 517063
12.4%
M 517061
12.4%
E 9145
 
0.2%
U 3149
 
0.1%
N 3121
 
0.1%
Other values (23) 7023
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4165668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1034285
24.8%
A 520421
12.5%
L 520061
12.5%
I 517198
12.4%
C 517141
12.4%
B 517063
12.4%
M 517061
12.4%
E 9145
 
0.2%
U 3149
 
0.1%
N 3121
 
0.1%
Other values (23) 7023
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4165668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1034285
24.8%
A 520421
12.5%
L 520061
12.5%
I 517198
12.4%
C 517141
12.4%
B 517063
12.4%
M 517061
12.4%
E 9145
 
0.2%
U 3149
 
0.1%
N 3121
 
0.1%
Other values (23) 7023
 
0.2%

ESTU_GENERO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size24.8 MiB
F
284160 
M
236138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters520298
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 284160
54.6%
M 236138
45.4%
(Missing) 9
 
< 0.1%

Length

2024-10-24T11:44:59.069229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:44:59.112479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
f 284160
54.6%
m 236138
45.4%

Most occurring characters

ValueCountFrequency (%)
F 284160
54.6%
M 236138
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 284160
54.6%
M 236138
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 284160
54.6%
M 236138
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 284160
54.6%
M 236138
45.4%
Distinct13239
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size35.1 MiB
2024-10-24T11:44:59.257847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length21.644018
Min length10

Characters and Unicode

Total characters11261534
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5434 ?
Unique (%)1.0%

Sample

1st row01/01/1985
2nd row01/01/1995
3rd row01/01/1997
4th row01/01/2001
5th row02/01/2001
ValueCountFrequency (%)
12:00:00 504872
33.0%
am 504872
33.0%
09/23/2003 951
 
0.1%
09/09/2003 895
 
0.1%
09/24/2003 851
 
0.1%
11/05/2003 844
 
0.1%
10/03/2003 843
 
0.1%
09/12/2003 838
 
0.1%
11/11/2003 836
 
0.1%
08/29/2003 833
 
0.1%
Other values (11018) 513416
33.6%
2024-10-24T11:44:59.481794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3677351
32.7%
2 1419152
 
12.6%
/ 1040614
 
9.2%
1 1022764
 
9.1%
1009744
 
9.0%
: 1009744
 
9.0%
A 504872
 
4.5%
M 504872
 
4.5%
3 342077
 
3.0%
4 204953
 
1.8%
Other values (5) 525391
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11261534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3677351
32.7%
2 1419152
 
12.6%
/ 1040614
 
9.2%
1 1022764
 
9.1%
1009744
 
9.0%
: 1009744
 
9.0%
A 504872
 
4.5%
M 504872
 
4.5%
3 342077
 
3.0%
4 204953
 
1.8%
Other values (5) 525391
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11261534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3677351
32.7%
2 1419152
 
12.6%
/ 1040614
 
9.2%
1 1022764
 
9.1%
1009744
 
9.0%
: 1009744
 
9.0%
A 504872
 
4.5%
M 504872
 
4.5%
3 342077
 
3.0%
4 204953
 
1.8%
Other values (5) 525391
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11261534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3677351
32.7%
2 1419152
 
12.6%
/ 1040614
 
9.2%
1 1022764
 
9.1%
1009744
 
9.0%
: 1009744
 
9.0%
A 504872
 
4.5%
M 504872
 
4.5%
3 342077
 
3.0%
4 204953
 
1.8%
Other values (5) 525391
 
4.7%

PERIODO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.8 MiB
20204
504872 
20201
 
15435

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2601535
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201
2nd row20201
3rd row20201
4th row20201
5th row20201

Common Values

ValueCountFrequency (%)
20204 504872
97.0%
20201 15435
 
3.0%

Length

2024-10-24T11:44:59.553067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:44:59.588808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
20204 504872
97.0%
20201 15435
 
3.0%

Most occurring characters

ValueCountFrequency (%)
2 1040614
40.0%
0 1040614
40.0%
4 504872
19.4%
1 15435
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2601535
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1040614
40.0%
0 1040614
40.0%
4 504872
19.4%
1 15435
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2601535
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1040614
40.0%
0 1040614
40.0%
4 504872
19.4%
1 15435
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2601535
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1040614
40.0%
0 1040614
40.0%
4 504872
19.4%
1 15435
 
0.6%

ESTU_CONSECUTIVO
Text

UNIQUE 

Distinct520307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size32.3 MiB
2024-10-24T11:44:59.855956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters8324912
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique520307 ?
Unique (%)100.0%

Sample

1st rowSB11202010045555
2nd rowSB11202010045719
3rd rowSB11202010070662
4th rowSB11202010069926
5th rowSB11202010023181
ValueCountFrequency (%)
sb11202010045555 1
 
< 0.1%
sb11202010071741 1
 
< 0.1%
sb11202010023181 1
 
< 0.1%
sb11202010057992 1
 
< 0.1%
sb11202010074718 1
 
< 0.1%
sb11202010070513 1
 
< 0.1%
sb11202010067334 1
 
< 0.1%
sb11202010069900 1
 
< 0.1%
sb11202010073927 1
 
< 0.1%
sb11202010002690 1
 
< 0.1%
Other values (520297) 520297
> 99.9%
2024-10-24T11:45:00.210998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1928651
23.2%
1 1412418
17.0%
2 1396900
16.8%
4 851245
10.2%
S 520307
 
6.2%
B 520307
 
6.2%
3 352733
 
4.2%
5 319463
 
3.8%
7 259326
 
3.1%
6 259153
 
3.1%
Other values (2) 504409
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8324912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1928651
23.2%
1 1412418
17.0%
2 1396900
16.8%
4 851245
10.2%
S 520307
 
6.2%
B 520307
 
6.2%
3 352733
 
4.2%
5 319463
 
3.8%
7 259326
 
3.1%
6 259153
 
3.1%
Other values (2) 504409
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8324912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1928651
23.2%
1 1412418
17.0%
2 1396900
16.8%
4 851245
10.2%
S 520307
 
6.2%
B 520307
 
6.2%
3 352733
 
4.2%
5 319463
 
3.8%
7 259326
 
3.1%
6 259153
 
3.1%
Other values (2) 504409
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8324912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1928651
23.2%
1 1412418
17.0%
2 1396900
16.8%
4 851245
10.2%
S 520307
 
6.2%
B 520307
 
6.2%
3 352733
 
4.2%
5 319463
 
3.8%
7 259326
 
3.1%
6 259153
 
3.1%
Other values (2) 504409
 
6.1%

ESTU_ESTUDIANTE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
ESTUDIANTE
520307 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5203070
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTUDIANTE
2nd rowESTUDIANTE
3rd rowESTUDIANTE
4th rowESTUDIANTE
5th rowESTUDIANTE

Common Values

ValueCountFrequency (%)
ESTUDIANTE 520307
100.0%

Length

2024-10-24T11:45:00.281015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:00.317577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
estudiante 520307
100.0%

Most occurring characters

ValueCountFrequency (%)
E 1040614
20.0%
T 1040614
20.0%
S 520307
10.0%
U 520307
10.0%
D 520307
10.0%
I 520307
10.0%
A 520307
10.0%
N 520307
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5203070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1040614
20.0%
T 1040614
20.0%
S 520307
10.0%
U 520307
10.0%
D 520307
10.0%
I 520307
10.0%
A 520307
10.0%
N 520307
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5203070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1040614
20.0%
T 1040614
20.0%
S 520307
10.0%
U 520307
10.0%
D 520307
10.0%
I 520307
10.0%
A 520307
10.0%
N 520307
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5203070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1040614
20.0%
T 1040614
20.0%
S 520307
10.0%
U 520307
10.0%
D 520307
10.0%
I 520307
10.0%
A 520307
10.0%
N 520307
10.0%

ESTU_PAIS_RESIDE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.3 MiB
COLOMBIA
517029 
VENEZUELA
 
2975
ESTADOS UNIDOS
 
62
ECUADOR
 
45
ESPAÑA
 
41
Other values (43)
 
155

Length

Max length31
Median length8
Mean length8.0061733
Min length4

Characters and Unicode

Total characters4165668
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA

Common Values

ValueCountFrequency (%)
COLOMBIA 517029
99.4%
VENEZUELA 2975
 
0.6%
ESTADOS UNIDOS 62
 
< 0.1%
ECUADOR 45
 
< 0.1%
ESPAÑA 41
 
< 0.1%
ARGENTINA 14
 
< 0.1%
CUBA 10
 
< 0.1%
MÉXICO 10
 
< 0.1%
BRASIL 10
 
< 0.1%
PERÚ 9
 
< 0.1%
Other values (38) 102
 
< 0.1%

Length

2024-10-24T11:45:00.360626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colombia 517029
99.3%
venezuela 2975
 
0.6%
estados 63
 
< 0.1%
unidos 62
 
< 0.1%
ecuador 45
 
< 0.1%
españa 41
 
< 0.1%
argentina 14
 
< 0.1%
cuba 10
 
< 0.1%
méxico 10
 
< 0.1%
brasil 10
 
< 0.1%
Other values (54) 153
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 1034285
24.8%
A 520421
12.5%
L 520061
12.5%
I 517198
12.4%
C 517141
12.4%
B 517063
12.4%
M 517061
12.4%
E 9145
 
0.2%
U 3149
 
0.1%
N 3121
 
0.1%
Other values (23) 7023
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4165668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1034285
24.8%
A 520421
12.5%
L 520061
12.5%
I 517198
12.4%
C 517141
12.4%
B 517063
12.4%
M 517061
12.4%
E 9145
 
0.2%
U 3149
 
0.1%
N 3121
 
0.1%
Other values (23) 7023
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4165668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1034285
24.8%
A 520421
12.5%
L 520061
12.5%
I 517198
12.4%
C 517141
12.4%
B 517063
12.4%
M 517061
12.4%
E 9145
 
0.2%
U 3149
 
0.1%
N 3121
 
0.1%
Other values (23) 7023
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4165668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1034285
24.8%
A 520421
12.5%
L 520061
12.5%
I 517198
12.4%
C 517141
12.4%
B 517063
12.4%
M 517061
12.4%
E 9145
 
0.2%
U 3149
 
0.1%
N 3121
 
0.1%
Other values (23) 7023
 
0.2%

ESTU_TIENEETNIA
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing3757
Missing (%)0.7%
Memory size25.3 MiB
No
484456 
Si
 
32094

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1033100
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 484456
93.1%
Si 32094
 
6.2%
(Missing) 3757
 
0.7%

Length

2024-10-24T11:45:00.407411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:00.444893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 484456
93.8%
si 32094
 
6.2%

Most occurring characters

ValueCountFrequency (%)
N 484456
46.9%
o 484456
46.9%
S 32094
 
3.1%
i 32094
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1033100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 484456
46.9%
o 484456
46.9%
S 32094
 
3.1%
i 32094
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1033100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 484456
46.9%
o 484456
46.9%
S 32094
 
3.1%
i 32094
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1033100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 484456
46.9%
o 484456
46.9%
S 32094
 
3.1%
i 32094
 
3.1%

ESTU_DEPTO_RESIDE
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)< 0.1%
Missing766
Missing (%)0.1%
Memory size30.2 MiB
BOGOTÁ
80859 
ANTIOQUIA
71198 
VALLE
41743 
CUNDINAMARCA
34426 
ATLANTICO
29153 
Other values (29)
262162 

Length

Max length15
Median length12
Mean length7.4999836
Min length4

Characters and Unicode

Total characters3896549
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCESAR
2nd rowNARIÑO
3rd rowCAUCA
4th rowPUTUMAYO
5th rowRISARALDA

Common Values

ValueCountFrequency (%)
BOGOTÁ 80859
15.5%
ANTIOQUIA 71198
13.7%
VALLE 41743
 
8.0%
CUNDINAMARCA 34426
 
6.6%
ATLANTICO 29153
 
5.6%
BOLIVAR 24380
 
4.7%
SANTANDER 23935
 
4.6%
CORDOBA 19489
 
3.7%
NARIÑO 15894
 
3.1%
TOLIMA 15833
 
3.0%
Other values (24) 162631
31.3%

Length

2024-10-24T11:45:00.601163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogotá 80859
14.9%
antioquia 71198
13.1%
valle 41743
 
7.7%
santander 38754
 
7.1%
cundinamarca 34426
 
6.3%
atlantico 29153
 
5.4%
bolivar 24380
 
4.5%
cordoba 19489
 
3.6%
nariño 15894
 
2.9%
tolima 15833
 
2.9%
Other values (26) 171217
31.5%

Most occurring characters

ValueCountFrequency (%)
A 754888
19.4%
O 406165
10.4%
N 306269
 
7.9%
I 305334
 
7.8%
T 299157
 
7.7%
C 213860
 
5.5%
L 207954
 
5.3%
R 207468
 
5.3%
U 170834
 
4.4%
E 156509
 
4.0%
Other values (16) 868111
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3896549
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 754888
19.4%
O 406165
10.4%
N 306269
 
7.9%
I 305334
 
7.8%
T 299157
 
7.7%
C 213860
 
5.5%
L 207954
 
5.3%
R 207468
 
5.3%
U 170834
 
4.4%
E 156509
 
4.0%
Other values (16) 868111
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3896549
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 754888
19.4%
O 406165
10.4%
N 306269
 
7.9%
I 305334
 
7.8%
T 299157
 
7.7%
C 213860
 
5.5%
L 207954
 
5.3%
R 207468
 
5.3%
U 170834
 
4.4%
E 156509
 
4.0%
Other values (16) 868111
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3896549
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 754888
19.4%
O 406165
10.4%
N 306269
 
7.9%
I 305334
 
7.8%
T 299157
 
7.7%
C 213860
 
5.5%
L 207954
 
5.3%
R 207468
 
5.3%
U 170834
 
4.4%
E 156509
 
4.0%
Other values (16) 868111
22.3%

ESTU_COD_RESIDE_DEPTO
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct34
Distinct (%)< 0.1%
Missing766
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean36.332692
Minimum5
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:00.651814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median20
Q354
95-th percentile76
Maximum99999
Range99994
Interquartile range (IQR)43

Descriptive statistics

Standard deviation605.10582
Coefficient of variation (CV)16.654583
Kurtosis27234.401
Mean36.332692
Median Absolute Deviation (MAD)15
Skewness164.8754
Sum18876323
Variance366153.05
MonotonicityNot monotonic
2024-10-24T11:45:00.701244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
11 80859
15.5%
5 71198
13.7%
76 41743
 
8.0%
25 34426
 
6.6%
8 29153
 
5.6%
13 24380
 
4.7%
68 23935
 
4.6%
23 19489
 
3.7%
52 15894
 
3.1%
73 15833
 
3.0%
Other values (24) 162631
31.3%
ValueCountFrequency (%)
5 71198
13.7%
8 29153
 
5.6%
11 80859
15.5%
13 24380
 
4.7%
15 15345
 
2.9%
17 10109
 
1.9%
18 3891
 
0.7%
19 13226
 
2.5%
20 12681
 
2.4%
23 19489
 
3.7%
ValueCountFrequency (%)
99999 19
 
< 0.1%
99 516
 
0.1%
97 322
 
0.1%
95 813
 
0.2%
94 285
 
0.1%
91 638
 
0.1%
88 593
 
0.1%
86 3696
0.7%
85 5521
1.1%
81 3001
0.6%
Distinct1031
Distinct (%)0.2%
Missing766
Missing (%)0.1%
Memory size34.7 MiB
2024-10-24T11:45:00.901950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length9.1884221
Min length3

Characters and Unicode

Total characters4773762
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSAN DIEGO
2nd rowIPIALES
3rd rowTOTORÓ
4th rowMOCOA
5th rowPEREIRA
ValueCountFrequency (%)
bogotá 80859
 
11.0%
d.c 80859
 
11.0%
de 31403
 
4.3%
medellín 27891
 
3.8%
cali 19804
 
2.7%
san 19377
 
2.6%
barranquilla 15168
 
2.1%
cartagena 13209
 
1.8%
indias 13011
 
1.8%
la 10076
 
1.4%
Other values (1021) 426161
57.8%
2024-10-24T11:45:01.190940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 691815
14.5%
O 368018
 
7.7%
E 296398
 
6.2%
L 281797
 
5.9%
C 272533
 
5.7%
N 260213
 
5.5%
I 251571
 
5.3%
R 244220
 
5.1%
D 232782
 
4.9%
T 225317
 
4.7%
Other values (25) 1649098
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4773762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 691815
14.5%
O 368018
 
7.7%
E 296398
 
6.2%
L 281797
 
5.9%
C 272533
 
5.7%
N 260213
 
5.5%
I 251571
 
5.3%
R 244220
 
5.1%
D 232782
 
4.9%
T 225317
 
4.7%
Other values (25) 1649098
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4773762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 691815
14.5%
O 368018
 
7.7%
E 296398
 
6.2%
L 281797
 
5.9%
C 272533
 
5.7%
N 260213
 
5.5%
I 251571
 
5.3%
R 244220
 
5.1%
D 232782
 
4.9%
T 225317
 
4.7%
Other values (25) 1649098
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4773762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 691815
14.5%
O 368018
 
7.7%
E 296398
 
6.2%
L 281797
 
5.9%
C 272533
 
5.7%
N 260213
 
5.5%
I 251571
 
5.3%
R 244220
 
5.1%
D 232782
 
4.9%
T 225317
 
4.7%
Other values (25) 1649098
34.5%

ESTU_COD_RESIDE_MCPIO
Real number (ℝ)

HIGH CORRELATION 

Distinct1116
Distinct (%)0.2%
Missing766
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean32913.911
Minimum5001
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:01.268717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median20621
Q354001
95-th percentile76364
Maximum99999
Range94998
Interquartile range (IQR)43000

Descriptive statistics

Standard deviation26569.979
Coefficient of variation (CV)0.80725683
Kurtosis-1.1429209
Mean32913.911
Median Absolute Deviation (MAD)15006
Skewness0.62885799
Sum1.7100126 × 1010
Variance7.059638 × 108
MonotonicityNot monotonic
2024-10-24T11:45:01.323934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 80859
 
15.5%
5001 27891
 
5.4%
76001 19804
 
3.8%
8001 15168
 
2.9%
13001 13011
 
2.5%
54001 7942
 
1.5%
25754 7765
 
1.5%
73001 7162
 
1.4%
8758 6283
 
1.2%
68001 6199
 
1.2%
Other values (1106) 327457
62.9%
ValueCountFrequency (%)
5001 27891
5.4%
5002 202
 
< 0.1%
5004 20
 
< 0.1%
5021 41
 
< 0.1%
5030 265
 
0.1%
5031 258
 
< 0.1%
5034 462
 
0.1%
5036 74
 
< 0.1%
5038 126
 
< 0.1%
5040 224
 
< 0.1%
ValueCountFrequency (%)
99999 19
 
< 0.1%
99773 176
< 0.1%
99624 42
 
< 0.1%
99524 115
< 0.1%
99001 183
< 0.1%
97889 19
 
< 0.1%
97666 17
 
< 0.1%
97511 7
 
< 0.1%
97161 37
 
< 0.1%
97001 242
< 0.1%

FAMI_ESTRATOVIVIENDA
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing18510
Missing (%)3.6%
Memory size28.8 MiB
Estrato 2
184640 
Estrato 1
153646 
Estrato 3
106862 
Estrato 4
26258 
Sin Estrato
 
15778
Other values (2)
 
14613

Length

Max length11
Median length9
Mean length9.062886
Min length9

Characters and Unicode

Total characters4547729
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstrato 1
2nd rowEstrato 1
3rd rowEstrato 1
4th rowEstrato 6
5th rowEstrato 1

Common Values

ValueCountFrequency (%)
Estrato 2 184640
35.5%
Estrato 1 153646
29.5%
Estrato 3 106862
20.5%
Estrato 4 26258
 
5.0%
Sin Estrato 15778
 
3.0%
Estrato 5 9396
 
1.8%
Estrato 6 5217
 
1.0%
(Missing) 18510
 
3.6%

Length

2024-10-24T11:45:01.377357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:01.427178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
estrato 501797
50.0%
2 184640
 
18.4%
1 153646
 
15.3%
3 106862
 
10.6%
4 26258
 
2.6%
sin 15778
 
1.6%
5 9396
 
0.9%
6 5217
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t 1003594
22.1%
E 501797
11.0%
s 501797
11.0%
r 501797
11.0%
a 501797
11.0%
o 501797
11.0%
501797
11.0%
2 184640
 
4.1%
1 153646
 
3.4%
3 106862
 
2.3%
Other values (6) 88205
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4547729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1003594
22.1%
E 501797
11.0%
s 501797
11.0%
r 501797
11.0%
a 501797
11.0%
o 501797
11.0%
501797
11.0%
2 184640
 
4.1%
1 153646
 
3.4%
3 106862
 
2.3%
Other values (6) 88205
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4547729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1003594
22.1%
E 501797
11.0%
s 501797
11.0%
r 501797
11.0%
a 501797
11.0%
o 501797
11.0%
501797
11.0%
2 184640
 
4.1%
1 153646
 
3.4%
3 106862
 
2.3%
Other values (6) 88205
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4547729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1003594
22.1%
E 501797
11.0%
s 501797
11.0%
r 501797
11.0%
a 501797
11.0%
o 501797
11.0%
501797
11.0%
2 184640
 
4.1%
1 153646
 
3.4%
3 106862
 
2.3%
Other values (6) 88205
 
1.9%

FAMI_PERSONASHOGAR
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing16580
Missing (%)3.2%
Memory size27.3 MiB
3 a 4
248845 
5 a 6
158650 
7 a 8
41617 
1 a 2
38169 
9 o más
 
16446

Length

Max length7
Median length5
Mean length5.0652973
Min length5

Characters and Unicode

Total characters2551527
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5 a 6
2nd row3 a 4
3rd row1 a 2
4th row3 a 4
5th row7 a 8

Common Values

ValueCountFrequency (%)
3 a 4 248845
47.8%
5 a 6 158650
30.5%
7 a 8 41617
 
8.0%
1 a 2 38169
 
7.3%
9 o más 16446
 
3.2%
(Missing) 16580
 
3.2%

Length

2024-10-24T11:45:01.483042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:01.529881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 487281
32.2%
3 248845
16.5%
4 248845
16.5%
5 158650
 
10.5%
6 158650
 
10.5%
7 41617
 
2.8%
8 41617
 
2.8%
1 38169
 
2.5%
2 38169
 
2.5%
9 16446
 
1.1%
Other values (2) 32892
 
2.2%

Most occurring characters

ValueCountFrequency (%)
1007454
39.5%
a 487281
19.1%
3 248845
 
9.8%
4 248845
 
9.8%
5 158650
 
6.2%
6 158650
 
6.2%
7 41617
 
1.6%
8 41617
 
1.6%
1 38169
 
1.5%
2 38169
 
1.5%
Other values (5) 82230
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2551527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1007454
39.5%
a 487281
19.1%
3 248845
 
9.8%
4 248845
 
9.8%
5 158650
 
6.2%
6 158650
 
6.2%
7 41617
 
1.6%
8 41617
 
1.6%
1 38169
 
1.5%
2 38169
 
1.5%
Other values (5) 82230
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2551527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1007454
39.5%
a 487281
19.1%
3 248845
 
9.8%
4 248845
 
9.8%
5 158650
 
6.2%
6 158650
 
6.2%
7 41617
 
1.6%
8 41617
 
1.6%
1 38169
 
1.5%
2 38169
 
1.5%
Other values (5) 82230
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2551527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1007454
39.5%
a 487281
19.1%
3 248845
 
9.8%
4 248845
 
9.8%
5 158650
 
6.2%
6 158650
 
6.2%
7 41617
 
1.6%
8 41617
 
1.6%
1 38169
 
1.5%
2 38169
 
1.5%
Other values (5) 82230
 
3.2%

FAMI_CUARTOSHOGAR
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing17278
Missing (%)3.3%
Memory size26.4 MiB
Tres
195843 
Dos
174505 
Cuatro
69859 
Uno
25721 
Cinco
23664 

Length

Max length10
Median length6
Mean length4.0870288
Min length3

Characters and Unicode

Total characters2055894
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUno
2nd rowCuatro
3rd rowUno
4th rowCuatro
5th rowCuatro

Common Values

ValueCountFrequency (%)
Tres 195843
37.6%
Dos 174505
33.5%
Cuatro 69859
 
13.4%
Uno 25721
 
4.9%
Cinco 23664
 
4.5%
Seis o mas 13437
 
2.6%
(Missing) 17278
 
3.3%

Length

2024-10-24T11:45:01.581997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:01.628215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tres 195843
37.0%
dos 174505
32.9%
cuatro 69859
 
13.2%
uno 25721
 
4.9%
cinco 23664
 
4.5%
seis 13437
 
2.5%
o 13437
 
2.5%
mas 13437
 
2.5%

Most occurring characters

ValueCountFrequency (%)
s 397222
19.3%
o 307186
14.9%
r 265702
12.9%
e 209280
10.2%
T 195843
9.5%
D 174505
8.5%
C 93523
 
4.5%
a 83296
 
4.1%
u 69859
 
3.4%
t 69859
 
3.4%
Other values (7) 189619
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2055894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 397222
19.3%
o 307186
14.9%
r 265702
12.9%
e 209280
10.2%
T 195843
9.5%
D 174505
8.5%
C 93523
 
4.5%
a 83296
 
4.1%
u 69859
 
3.4%
t 69859
 
3.4%
Other values (7) 189619
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2055894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 397222
19.3%
o 307186
14.9%
r 265702
12.9%
e 209280
10.2%
T 195843
9.5%
D 174505
8.5%
C 93523
 
4.5%
a 83296
 
4.1%
u 69859
 
3.4%
t 69859
 
3.4%
Other values (7) 189619
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2055894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 397222
19.3%
o 307186
14.9%
r 265702
12.9%
e 209280
10.2%
T 195843
9.5%
D 174505
8.5%
C 93523
 
4.5%
a 83296
 
4.1%
u 69859
 
3.4%
t 69859
 
3.4%
Other values (7) 189619
9.2%

FAMI_EDUCACIONPADRE
Categorical

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing14539
Missing (%)2.8%
Memory size41.6 MiB
Secundaria (Bachillerato) completa
122645 
Primaria incompleta
92766 
Secundaria (Bachillerato) incompleta
66102 
Educación profesional completa
50745 
Primaria completa
46305 
Other values (7)
127205 

Length

Max length36
Median length32
Mean length25.442565
Min length7

Characters and Unicode

Total characters12868035
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimaria incompleta
2nd rowSecundaria (Bachillerato) incompleta
3rd rowTécnica o tecnológica completa
4th rowEducación profesional completa
5th rowPrimaria completa

Common Values

ValueCountFrequency (%)
Secundaria (Bachillerato) completa 122645
23.6%
Primaria incompleta 92766
17.8%
Secundaria (Bachillerato) incompleta 66102
12.7%
Educación profesional completa 50745
9.8%
Primaria completa 46305
 
8.9%
Técnica o tecnológica completa 34084
 
6.6%
No sabe 30673
 
5.9%
Ninguno 21007
 
4.0%
Postgrado 12302
 
2.4%
Educación profesional incompleta 10805
 
2.1%
Other values (2) 18334
 
3.5%
(Missing) 14539
 
2.8%

Length

2024-10-24T11:45:01.680816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa 253779
19.3%
secundaria 188747
14.3%
bachillerato 188747
14.3%
incompleta 180133
13.7%
primaria 139071
10.6%
educación 61550
 
4.7%
profesional 61550
 
4.7%
técnica 44544
 
3.4%
o 44544
 
3.4%
tecnológica 44544
 
3.4%
Other values (5) 110403
8.4%

Most occurring characters

ValueCountFrequency (%)
a 1730079
13.4%
c 1120556
 
8.7%
i 1076838
 
8.4%
e 948173
 
7.4%
l 925374
 
7.2%
o 919005
 
7.1%
811844
 
6.3%
r 729488
 
5.7%
t 679505
 
5.3%
n 623082
 
4.8%
Other values (20) 3304091
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12868035
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1730079
13.4%
c 1120556
 
8.7%
i 1076838
 
8.4%
e 948173
 
7.4%
l 925374
 
7.2%
o 919005
 
7.1%
811844
 
6.3%
r 729488
 
5.7%
t 679505
 
5.3%
n 623082
 
4.8%
Other values (20) 3304091
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12868035
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1730079
13.4%
c 1120556
 
8.7%
i 1076838
 
8.4%
e 948173
 
7.4%
l 925374
 
7.2%
o 919005
 
7.1%
811844
 
6.3%
r 729488
 
5.7%
t 679505
 
5.3%
n 623082
 
4.8%
Other values (20) 3304091
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12868035
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1730079
13.4%
c 1120556
 
8.7%
i 1076838
 
8.4%
e 948173
 
7.4%
l 925374
 
7.2%
o 919005
 
7.1%
811844
 
6.3%
r 729488
 
5.7%
t 679505
 
5.3%
n 623082
 
4.8%
Other values (20) 3304091
25.7%

FAMI_EDUCACIONMADRE
Categorical

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing15030
Missing (%)2.9%
Memory size44.4 MiB
Secundaria (Bachillerato) completa
139724 
Primaria incompleta
71811 
Secundaria (Bachillerato) incompleta
68862 
Educación profesional completa
61224 
Técnica o tecnológica completa
51814 
Other values (7)
111842 

Length

Max length36
Median length34
Mean length27.673104
Min length7

Characters and Unicode

Total characters13982583
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimaria incompleta
2nd rowSecundaria (Bachillerato) incompleta
3rd rowEducación profesional completa
4th rowSecundaria (Bachillerato) completa
5th rowPrimaria incompleta

Common Values

ValueCountFrequency (%)
Secundaria (Bachillerato) completa 139724
26.9%
Primaria incompleta 71811
13.8%
Secundaria (Bachillerato) incompleta 68862
13.2%
Educación profesional completa 61224
11.8%
Técnica o tecnológica completa 51814
 
10.0%
Primaria completa 46328
 
8.9%
Técnica o tecnológica incompleta 15539
 
3.0%
Postgrado 13810
 
2.7%
Educación profesional incompleta 13309
 
2.6%
Ninguno 12474
 
2.4%
Other values (2) 10382
 
2.0%
(Missing) 15030
 
2.9%

Length

2024-10-24T11:45:01.730484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa 299090
21.3%
secundaria 208586
14.9%
bachillerato 208586
14.9%
incompleta 169521
12.1%
primaria 118139
 
8.4%
educación 74533
 
5.3%
profesional 74533
 
5.3%
técnica 67353
 
4.8%
o 67353
 
4.8%
tecnológica 67353
 
4.8%
Other values (5) 47048
 
3.4%

Most occurring characters

ValueCountFrequency (%)
a 1847197
13.2%
c 1305457
 
9.3%
i 1120413
 
8.0%
e 1036855
 
7.4%
l 1028865
 
7.4%
o 1011445
 
7.2%
896818
 
6.4%
t 758360
 
5.4%
r 741793
 
5.3%
n 686827
 
4.9%
Other values (20) 3548553
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13982583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1847197
13.2%
c 1305457
 
9.3%
i 1120413
 
8.0%
e 1036855
 
7.4%
l 1028865
 
7.4%
o 1011445
 
7.2%
896818
 
6.4%
t 758360
 
5.4%
r 741793
 
5.3%
n 686827
 
4.9%
Other values (20) 3548553
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13982583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1847197
13.2%
c 1305457
 
9.3%
i 1120413
 
8.0%
e 1036855
 
7.4%
l 1028865
 
7.4%
o 1011445
 
7.2%
896818
 
6.4%
t 758360
 
5.4%
r 741793
 
5.3%
n 686827
 
4.9%
Other values (20) 3548553
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13982583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1847197
13.2%
c 1305457
 
9.3%
i 1120413
 
8.0%
e 1036855
 
7.4%
l 1028865
 
7.4%
o 1011445
 
7.2%
896818
 
6.4%
t 758360
 
5.4%
r 741793
 
5.3%
n 686827
 
4.9%
Other values (20) 3548553
25.4%

FAMI_TRABAJOLABORPADRE
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing21147
Missing (%)4.1%
Memory size75.4 MiB
Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etc
88423 
Es agricultor, pesquero o jornalero
67581 
Trabaja por cuenta propia (por ejemplo plomero, electricista)
58130 
Es operario de máquinas o conduce vehículos (taxita, chofer)
47985 
No sabe
45902 
Other values (8)
191139 

Length

Max length100
Median length74
Mean length58.201338
Min length7

Characters and Unicode

Total characters29051780
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrabaja en el hogar, no trabaja o estudia
2nd rowTrabaja en el hogar, no trabaja o estudia
3rd rowEs dueño de un negocio grande, tiene un cargo de nivel directivo o gerencial
4th rowTrabaja como profesional (por ejemplo médico, abogado, ingeniero)
5th rowTrabaja como personal de limpieza, mantenimiento, seguridad o construcción

Common Values

ValueCountFrequency (%)
Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etc 88423
17.0%
Es agricultor, pesquero o jornalero 67581
13.0%
Trabaja por cuenta propia (por ejemplo plomero, electricista) 58130
11.2%
Es operario de máquinas o conduce vehículos (taxita, chofer) 47985
9.2%
No sabe 45902
8.8%
Trabaja como profesional (por ejemplo médico, abogado, ingeniero) 41910
8.1%
Trabaja como personal de limpieza, mantenimiento, seguridad o construcción 33380
 
6.4%
Trabaja en el hogar, no trabaja o estudia 32748
 
6.3%
Es vendedor o trabaja en atención al público 31627
 
6.1%
Tiene un trabajo de tipo auxiliar administrativo (por ejemplo, secretario o asistente) 22026
 
4.2%
Other values (3) 29448
 
5.7%
(Missing) 21147
 
4.1%

Length

2024-10-24T11:45:01.777441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
o 338642
 
7.6%
por 268619
 
6.0%
es 250488
 
5.6%
trabaja 230543
 
5.2%
de 221558
 
5.0%
tiene 213744
 
4.8%
ejemplo 210489
 
4.7%
no 168066
 
3.8%
un 140193
 
3.1%
dueño 103295
 
2.3%
Other values (52) 2322957
52.0%

Most occurring characters

ValueCountFrequency (%)
3969434
13.7%
e 3350532
11.5%
o 3077228
 
10.6%
a 2217108
 
7.6%
r 1661788
 
5.7%
i 1505582
 
5.2%
n 1489607
 
5.1%
p 1374495
 
4.7%
t 1104449
 
3.8%
c 1062281
 
3.7%
Other values (27) 8239276
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29051780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3969434
13.7%
e 3350532
11.5%
o 3077228
 
10.6%
a 2217108
 
7.6%
r 1661788
 
5.7%
i 1505582
 
5.2%
n 1489607
 
5.1%
p 1374495
 
4.7%
t 1104449
 
3.8%
c 1062281
 
3.7%
Other values (27) 8239276
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29051780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3969434
13.7%
e 3350532
11.5%
o 3077228
 
10.6%
a 2217108
 
7.6%
r 1661788
 
5.7%
i 1505582
 
5.2%
n 1489607
 
5.1%
p 1374495
 
4.7%
t 1104449
 
3.8%
c 1062281
 
3.7%
Other values (27) 8239276
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29051780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3969434
13.7%
e 3350532
11.5%
o 3077228
 
10.6%
a 2217108
 
7.6%
r 1661788
 
5.7%
i 1505582
 
5.2%
n 1489607
 
5.1%
p 1374495
 
4.7%
t 1104449
 
3.8%
c 1062281
 
3.7%
Other values (27) 8239276
28.4%

FAMI_TRABAJOLABORMADRE
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing19059
Missing (%)3.7%
Memory size72.2 MiB
Trabaja en el hogar, no trabaja o estudia
191474 
Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etc
75035 
Trabaja como profesional (por ejemplo médico, abogado, ingeniero)
44555 
Trabaja como personal de limpieza, mantenimiento, seguridad o construcción
41096 
Es vendedor o trabaja en atención al público
39414 
Other values (8)
109674 

Length

Max length100
Median length86
Mean length58.799822
Min length7

Characters and Unicode

Total characters29473293
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrabaja por cuenta propia (por ejemplo plomero, electricista)
2nd rowTrabaja en el hogar, no trabaja o estudia
3rd rowTrabaja como profesional (por ejemplo médico, abogado, ingeniero)
4th rowTrabaja en el hogar, no trabaja o estudia
5th rowTrabaja en el hogar, no trabaja o estudia

Common Values

ValueCountFrequency (%)
Trabaja en el hogar, no trabaja o estudia 191474
36.8%
Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etc 75035
 
14.4%
Trabaja como profesional (por ejemplo médico, abogado, ingeniero) 44555
 
8.6%
Trabaja como personal de limpieza, mantenimiento, seguridad o construcción 41096
 
7.9%
Es vendedor o trabaja en atención al público 39414
 
7.6%
Tiene un trabajo de tipo auxiliar administrativo (por ejemplo, secretario o asistente) 39408
 
7.6%
Trabaja por cuenta propia (por ejemplo plomero, electricista) 17895
 
3.4%
Es agricultor, pesquero o jornalero 16088
 
3.1%
No sabe 13651
 
2.6%
Es dueño de un negocio grande, tiene un cargo de nivel directivo o gerencial 10459
 
2.0%
Other values (3) 12173
 
2.3%
(Missing) 19059
 
3.7%

Length

2024-10-24T11:45:01.824911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
trabaja 525908
 
11.0%
o 420825
 
8.8%
no 280680
 
5.9%
en 230888
 
4.8%
tiene 199937
 
4.2%
por 194788
 
4.1%
estudia 191474
 
4.0%
el 191474
 
4.0%
hogar 191474
 
4.0%
de 184308
 
3.8%
Other values (52) 2182972
45.5%

Most occurring characters

ValueCountFrequency (%)
4293480
14.6%
e 3159087
 
10.7%
a 3120088
 
10.6%
o 2805476
 
9.5%
n 1659189
 
5.6%
r 1635571
 
5.5%
i 1510312
 
5.1%
t 1267453
 
4.3%
p 1030569
 
3.5%
d 933455
 
3.2%
Other values (27) 8058613
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29473293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4293480
14.6%
e 3159087
 
10.7%
a 3120088
 
10.6%
o 2805476
 
9.5%
n 1659189
 
5.6%
r 1635571
 
5.5%
i 1510312
 
5.1%
t 1267453
 
4.3%
p 1030569
 
3.5%
d 933455
 
3.2%
Other values (27) 8058613
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29473293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4293480
14.6%
e 3159087
 
10.7%
a 3120088
 
10.6%
o 2805476
 
9.5%
n 1659189
 
5.6%
r 1635571
 
5.5%
i 1510312
 
5.1%
t 1267453
 
4.3%
p 1030569
 
3.5%
d 933455
 
3.2%
Other values (27) 8058613
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29473293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4293480
14.6%
e 3159087
 
10.7%
a 3120088
 
10.6%
o 2805476
 
9.5%
n 1659189
 
5.6%
r 1635571
 
5.5%
i 1510312
 
5.1%
t 1267453
 
4.3%
p 1030569
 
3.5%
d 933455
 
3.2%
Other values (27) 8058613
27.3%

FAMI_TIENEINTERNET
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing15194
Missing (%)2.9%
Memory size25.4 MiB
Si
356056 
No
149057 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1010226
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 356056
68.4%
No 149057
28.6%
(Missing) 15194
 
2.9%

Length

2024-10-24T11:45:01.869120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:01.906870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 356056
70.5%
no 149057
29.5%

Most occurring characters

ValueCountFrequency (%)
S 356056
35.2%
i 356056
35.2%
N 149057
14.8%
o 149057
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1010226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 356056
35.2%
i 356056
35.2%
N 149057
14.8%
o 149057
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1010226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 356056
35.2%
i 356056
35.2%
N 149057
14.8%
o 149057
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1010226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 356056
35.2%
i 356056
35.2%
N 149057
14.8%
o 149057
14.8%

FAMI_TIENESERVICIOTV
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing16983
Missing (%)3.3%
Memory size25.4 MiB
Si
378156 
No
125168 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1006648
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSi
5th rowNo

Common Values

ValueCountFrequency (%)
Si 378156
72.7%
No 125168
 
24.1%
(Missing) 16983
 
3.3%

Length

2024-10-24T11:45:01.947009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:01.984092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 378156
75.1%
no 125168
 
24.9%

Most occurring characters

ValueCountFrequency (%)
S 378156
37.6%
i 378156
37.6%
N 125168
 
12.4%
o 125168
 
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1006648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 378156
37.6%
i 378156
37.6%
N 125168
 
12.4%
o 125168
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1006648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 378156
37.6%
i 378156
37.6%
N 125168
 
12.4%
o 125168
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1006648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 378156
37.6%
i 378156
37.6%
N 125168
 
12.4%
o 125168
 
12.4%

FAMI_TIENECOMPUTADOR
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing21616
Missing (%)4.2%
Memory size25.4 MiB
Si
306181 
No
192510 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters997382
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 306181
58.8%
No 192510
37.0%
(Missing) 21616
 
4.2%

Length

2024-10-24T11:45:02.026108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.062231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 306181
61.4%
no 192510
38.6%

Most occurring characters

ValueCountFrequency (%)
S 306181
30.7%
i 306181
30.7%
N 192510
19.3%
o 192510
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 997382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 306181
30.7%
i 306181
30.7%
N 192510
19.3%
o 192510
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 997382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 306181
30.7%
i 306181
30.7%
N 192510
19.3%
o 192510
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 997382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 306181
30.7%
i 306181
30.7%
N 192510
19.3%
o 192510
19.3%

FAMI_TIENELAVADORA
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17659
Missing (%)3.4%
Memory size25.4 MiB
Si
394856 
No
107792 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1005296
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 394856
75.9%
No 107792
 
20.7%
(Missing) 17659
 
3.4%

Length

2024-10-24T11:45:02.101564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.139669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si 394856
78.6%
no 107792
 
21.4%

Most occurring characters

ValueCountFrequency (%)
S 394856
39.3%
i 394856
39.3%
N 107792
 
10.7%
o 107792
 
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1005296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 394856
39.3%
i 394856
39.3%
N 107792
 
10.7%
o 107792
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1005296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 394856
39.3%
i 394856
39.3%
N 107792
 
10.7%
o 107792
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1005296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 394856
39.3%
i 394856
39.3%
N 107792
 
10.7%
o 107792
 
10.7%

FAMI_TIENEHORNOMICROOGAS
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing18579
Missing (%)3.6%
Memory size25.4 MiB
No
260187 
Si
241541 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1003456
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
No 260187
50.0%
Si 241541
46.4%
(Missing) 18579
 
3.6%

Length

2024-10-24T11:45:02.181620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.222189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 260187
51.9%
si 241541
48.1%

Most occurring characters

ValueCountFrequency (%)
N 260187
25.9%
o 260187
25.9%
S 241541
24.1%
i 241541
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1003456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 260187
25.9%
o 260187
25.9%
S 241541
24.1%
i 241541
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1003456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 260187
25.9%
o 260187
25.9%
S 241541
24.1%
i 241541
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1003456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 260187
25.9%
o 260187
25.9%
S 241541
24.1%
i 241541
24.1%

FAMI_TIENEAUTOMOVIL
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing19264
Missing (%)3.7%
Memory size25.4 MiB
No
364793 
Si
136250 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1002086
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSi
5th rowNo

Common Values

ValueCountFrequency (%)
No 364793
70.1%
Si 136250
 
26.2%
(Missing) 19264
 
3.7%

Length

2024-10-24T11:45:02.265018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.304520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 364793
72.8%
si 136250
 
27.2%

Most occurring characters

ValueCountFrequency (%)
N 364793
36.4%
o 364793
36.4%
S 136250
 
13.6%
i 136250
 
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1002086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 364793
36.4%
o 364793
36.4%
S 136250
 
13.6%
i 136250
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1002086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 364793
36.4%
o 364793
36.4%
S 136250
 
13.6%
i 136250
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1002086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 364793
36.4%
o 364793
36.4%
S 136250
 
13.6%
i 136250
 
13.6%

FAMI_TIENEMOTOCICLETA
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing18537
Missing (%)3.6%
Memory size25.4 MiB
No
285162 
Si
216608 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1003540
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 285162
54.8%
Si 216608
41.6%
(Missing) 18537
 
3.6%

Length

2024-10-24T11:45:02.349179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.386473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 285162
56.8%
si 216608
43.2%

Most occurring characters

ValueCountFrequency (%)
N 285162
28.4%
o 285162
28.4%
S 216608
21.6%
i 216608
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1003540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 285162
28.4%
o 285162
28.4%
S 216608
21.6%
i 216608
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1003540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 285162
28.4%
o 285162
28.4%
S 216608
21.6%
i 216608
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1003540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 285162
28.4%
o 285162
28.4%
S 216608
21.6%
i 216608
21.6%

FAMI_TIENECONSOLAVIDEOJUEGOS
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing18929
Missing (%)3.6%
Memory size25.4 MiB
No
388409 
Si
112969 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1002756
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 388409
74.6%
Si 112969
 
21.7%
(Missing) 18929
 
3.6%

Length

2024-10-24T11:45:02.427992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.464885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 388409
77.5%
si 112969
 
22.5%

Most occurring characters

ValueCountFrequency (%)
N 388409
38.7%
o 388409
38.7%
S 112969
 
11.3%
i 112969
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1002756
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 388409
38.7%
o 388409
38.7%
S 112969
 
11.3%
i 112969
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1002756
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 388409
38.7%
o 388409
38.7%
S 112969
 
11.3%
i 112969
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1002756
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 388409
38.7%
o 388409
38.7%
S 112969
 
11.3%
i 112969
 
11.3%

FAMI_NUMLIBROS
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing15606
Missing (%)3.0%
Memory size32.3 MiB
0 A 10 LIBROS
209334 
11 A 25 LIBROS
154997 
26 A 100 LIBROS
105493 
MÁS DE 100 LIBROS
34877 

Length

Max length17
Median length15
Mean length14.001565
Min length13

Characters and Unicode

Total characters7066604
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0 A 10 LIBROS
2nd row0 A 10 LIBROS
3rd row11 A 25 LIBROS
4th row26 A 100 LIBROS
5th row0 A 10 LIBROS

Common Values

ValueCountFrequency (%)
0 A 10 LIBROS 209334
40.2%
11 A 25 LIBROS 154997
29.8%
26 A 100 LIBROS 105493
20.3%
MÁS DE 100 LIBROS 34877
 
6.7%
(Missing) 15606
 
3.0%

Length

2024-10-24T11:45:02.509983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.557099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
libros 504701
25.0%
a 469824
23.3%
0 209334
10.4%
10 209334
10.4%
11 154997
 
7.7%
25 154997
 
7.7%
100 140370
 
7.0%
26 105493
 
5.2%
más 34877
 
1.7%
de 34877
 
1.7%

Most occurring characters

ValueCountFrequency (%)
1514103
21.4%
0 699408
9.9%
1 659698
9.3%
S 539578
 
7.6%
B 504701
 
7.1%
R 504701
 
7.1%
O 504701
 
7.1%
I 504701
 
7.1%
L 504701
 
7.1%
A 469824
 
6.6%
Other values (7) 660488
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7066604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1514103
21.4%
0 699408
9.9%
1 659698
9.3%
S 539578
 
7.6%
B 504701
 
7.1%
R 504701
 
7.1%
O 504701
 
7.1%
I 504701
 
7.1%
L 504701
 
7.1%
A 469824
 
6.6%
Other values (7) 660488
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7066604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1514103
21.4%
0 699408
9.9%
1 659698
9.3%
S 539578
 
7.6%
B 504701
 
7.1%
R 504701
 
7.1%
O 504701
 
7.1%
I 504701
 
7.1%
L 504701
 
7.1%
A 469824
 
6.6%
Other values (7) 660488
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7066604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1514103
21.4%
0 699408
9.9%
1 659698
9.3%
S 539578
 
7.6%
B 504701
 
7.1%
R 504701
 
7.1%
O 504701
 
7.1%
I 504701
 
7.1%
L 504701
 
7.1%
A 469824
 
6.6%
Other values (7) 660488
9.3%

FAMI_COMELECHEDERIVADOS
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing15265
Missing (%)2.9%
Memory size42.4 MiB
1 o 2 veces por semana
174703 
Todos o casi todos los días
149364 
3 a 5 veces por semana
132230 
Nunca o rara vez comemos eso
48745 

Length

Max length28
Median length22
Mean length24.057829
Min length22

Characters and Unicode

Total characters12150214
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 o 2 veces por semana
2nd row1 o 2 veces por semana
3rd row1 o 2 veces por semana
4th rowTodos o casi todos los días
5th row1 o 2 veces por semana

Common Values

ValueCountFrequency (%)
1 o 2 veces por semana 174703
33.6%
Todos o casi todos los días 149364
28.7%
3 a 5 veces por semana 132230
25.4%
Nunca o rara vez comemos eso 48745
 
9.4%
(Missing) 15265
 
2.9%

Length

2024-10-24T11:45:02.607560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.652048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
o 372812
12.3%
veces 306933
10.1%
por 306933
10.1%
semana 306933
10.1%
todos 298728
9.9%
1 174703
 
5.8%
2 174703
 
5.8%
los 149364
 
4.9%
días 149364
 
4.9%
casi 149364
 
4.9%
Other values (8) 640415
21.1%

Most occurring characters

ValueCountFrequency (%)
2525210
20.8%
o 1572800
12.9%
s 1458176
12.0%
a 1191059
9.8%
e 1067034
8.8%
c 553787
 
4.6%
d 448092
 
3.7%
r 404423
 
3.3%
m 404423
 
3.3%
n 355678
 
2.9%
Other values (14) 2169532
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12150214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2525210
20.8%
o 1572800
12.9%
s 1458176
12.0%
a 1191059
9.8%
e 1067034
8.8%
c 553787
 
4.6%
d 448092
 
3.7%
r 404423
 
3.3%
m 404423
 
3.3%
n 355678
 
2.9%
Other values (14) 2169532
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12150214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2525210
20.8%
o 1572800
12.9%
s 1458176
12.0%
a 1191059
9.8%
e 1067034
8.8%
c 553787
 
4.6%
d 448092
 
3.7%
r 404423
 
3.3%
m 404423
 
3.3%
n 355678
 
2.9%
Other values (14) 2169532
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12150214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2525210
20.8%
o 1572800
12.9%
s 1458176
12.0%
a 1191059
9.8%
e 1067034
8.8%
c 553787
 
4.6%
d 448092
 
3.7%
r 404423
 
3.3%
m 404423
 
3.3%
n 355678
 
2.9%
Other values (14) 2169532
17.9%

FAMI_COMECARNEPESCADOHUEVO
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing16841
Missing (%)3.2%
Memory size44.9 MiB
Todos o casi todos los días
204141 
3 a 5 veces por semana
154438 
1 o 2 veces por semana
122813 
Nunca o rara vez comemos eso
22074 

Length

Max length28
Median length22
Mean length24.290421
Min length22

Characters and Unicode

Total characters12229401
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 o 2 veces por semana
2nd row3 a 5 veces por semana
3rd rowTodos o casi todos los días
4th row3 a 5 veces por semana
5th rowNunca o rara vez comemos eso

Common Values

ValueCountFrequency (%)
Todos o casi todos los días 204141
39.2%
3 a 5 veces por semana 154438
29.7%
1 o 2 veces por semana 122813
23.6%
Nunca o rara vez comemos eso 22074
 
4.2%
(Missing) 16841
 
3.2%

Length

2024-10-24T11:45:02.704446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.748115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
todos 408282
13.5%
o 349028
11.6%
veces 277251
9.2%
semana 277251
9.2%
por 277251
9.2%
días 204141
 
6.8%
los 204141
 
6.8%
casi 204141
 
6.8%
3 154438
 
5.1%
a 154438
 
5.1%
Other values (8) 510434
16.9%

Most occurring characters

ValueCountFrequency (%)
2517330
20.6%
o 1713206
14.0%
s 1619355
13.2%
a 1183444
9.7%
e 897975
 
7.3%
d 612423
 
5.0%
c 525540
 
4.3%
r 321399
 
2.6%
m 321399
 
2.6%
v 299325
 
2.4%
Other values (14) 2218005
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12229401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2517330
20.6%
o 1713206
14.0%
s 1619355
13.2%
a 1183444
9.7%
e 897975
 
7.3%
d 612423
 
5.0%
c 525540
 
4.3%
r 321399
 
2.6%
m 321399
 
2.6%
v 299325
 
2.4%
Other values (14) 2218005
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12229401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2517330
20.6%
o 1713206
14.0%
s 1619355
13.2%
a 1183444
9.7%
e 897975
 
7.3%
d 612423
 
5.0%
c 525540
 
4.3%
r 321399
 
2.6%
m 321399
 
2.6%
v 299325
 
2.4%
Other values (14) 2218005
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12229401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2517330
20.6%
o 1713206
14.0%
s 1619355
13.2%
a 1183444
9.7%
e 897975
 
7.3%
d 612423
 
5.0%
c 525540
 
4.3%
r 321399
 
2.6%
m 321399
 
2.6%
v 299325
 
2.4%
Other values (14) 2218005
18.1%

FAMI_COMECEREALFRUTOSLEGUMBRE
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing21811
Missing (%)4.2%
Memory size39.1 MiB
1 o 2 veces por semana
198576 
3 a 5 veces por semana
146812 
Todos o casi todos los días
78210 
Nunca o rara vez comemos eso
74898 

Length

Max length28
Median length22
Mean length23.685947
Min length22

Characters and Unicode

Total characters11807350
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTodos o casi todos los días
2nd row1 o 2 veces por semana
3rd row3 a 5 veces por semana
4th row1 o 2 veces por semana
5th rowNunca o rara vez comemos eso

Common Values

ValueCountFrequency (%)
1 o 2 veces por semana 198576
38.2%
3 a 5 veces por semana 146812
28.2%
Todos o casi todos los días 78210
 
15.0%
Nunca o rara vez comemos eso 74898
 
14.4%
(Missing) 21811
 
4.2%

Length

2024-10-24T11:45:02.802198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:02.847160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
o 351684
11.8%
veces 345388
11.5%
por 345388
11.5%
semana 345388
11.5%
1 198576
 
6.6%
2 198576
 
6.6%
todos 156420
 
5.2%
a 146812
 
4.9%
5 146812
 
4.9%
3 146812
 
4.9%
Other values (8) 609120
20.4%

Most occurring characters

ValueCountFrequency (%)
2492480
21.1%
o 1312816
11.1%
e 1260858
10.7%
s 1231622
10.4%
a 1218702
10.3%
c 573394
 
4.9%
r 495184
 
4.2%
m 495184
 
4.2%
n 420286
 
3.6%
v 420286
 
3.6%
Other values (14) 1886538
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11807350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2492480
21.1%
o 1312816
11.1%
e 1260858
10.7%
s 1231622
10.4%
a 1218702
10.3%
c 573394
 
4.9%
r 495184
 
4.2%
m 495184
 
4.2%
n 420286
 
3.6%
v 420286
 
3.6%
Other values (14) 1886538
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11807350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2492480
21.1%
o 1312816
11.1%
e 1260858
10.7%
s 1231622
10.4%
a 1218702
10.3%
c 573394
 
4.9%
r 495184
 
4.2%
m 495184
 
4.2%
n 420286
 
3.6%
v 420286
 
3.6%
Other values (14) 1886538
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11807350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2492480
21.1%
o 1312816
11.1%
e 1260858
10.7%
s 1231622
10.4%
a 1218702
10.3%
c 573394
 
4.9%
r 495184
 
4.2%
m 495184
 
4.2%
n 420286
 
3.6%
v 420286
 
3.6%
Other values (14) 1886538
16.0%

FAMI_SITUACIONECONOMICA
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing17764
Missing (%)3.4%
Memory size26.7 MiB
Igual
299568 
Peor
112153 
Mejor
90822 

Length

Max length5
Median length5
Mean length4.776829
Min length4

Characters and Unicode

Total characters2400562
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMejor
2nd rowIgual
3rd rowIgual
4th rowIgual
5th rowPeor

Common Values

ValueCountFrequency (%)
Igual 299568
57.6%
Peor 112153
 
21.6%
Mejor 90822
 
17.5%
(Missing) 17764
 
3.4%

Length

2024-10-24T11:45:03.058874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:03.101834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
igual 299568
59.6%
peor 112153
 
22.3%
mejor 90822
 
18.1%

Most occurring characters

ValueCountFrequency (%)
I 299568
12.5%
g 299568
12.5%
u 299568
12.5%
a 299568
12.5%
l 299568
12.5%
e 202975
8.5%
o 202975
8.5%
r 202975
8.5%
P 112153
 
4.7%
M 90822
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2400562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 299568
12.5%
g 299568
12.5%
u 299568
12.5%
a 299568
12.5%
l 299568
12.5%
e 202975
8.5%
o 202975
8.5%
r 202975
8.5%
P 112153
 
4.7%
M 90822
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2400562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 299568
12.5%
g 299568
12.5%
u 299568
12.5%
a 299568
12.5%
l 299568
12.5%
e 202975
8.5%
o 202975
8.5%
r 202975
8.5%
P 112153
 
4.7%
M 90822
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2400562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 299568
12.5%
g 299568
12.5%
u 299568
12.5%
a 299568
12.5%
l 299568
12.5%
e 202975
8.5%
o 202975
8.5%
r 202975
8.5%
P 112153
 
4.7%
M 90822
 
3.8%

ESTU_DEDICACIONLECTURADIARIA
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing16013
Missing (%)3.1%
Memory size34.8 MiB
30 minutos o menos
193830 
Entre 30 y 60 minutos
136434 
No leo por entretenimiento
98915 
Entre 1 y 2 horas
52742 
Más de 2 horas
22373 

Length

Max length26
Median length21
Mean length20.098752
Min length14

Characters and Unicode

Total characters10135680
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo leo por entretenimiento
2nd row30 minutos o menos
3rd row30 minutos o menos
4th rowNo leo por entretenimiento
5th rowEntre 30 y 60 minutos

Common Values

ValueCountFrequency (%)
30 minutos o menos 193830
37.3%
Entre 30 y 60 minutos 136434
26.2%
No leo por entretenimiento 98915
19.0%
Entre 1 y 2 horas 52742
 
10.1%
Más de 2 horas 22373
 
4.3%
(Missing) 16013
 
3.1%

Length

2024-10-24T11:45:03.154153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:03.202498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
30 330264
15.0%
minutos 330264
15.0%
o 193830
8.8%
menos 193830
8.8%
entre 189176
8.6%
y 189176
8.6%
60 136434
 
6.2%
no 98915
 
4.5%
leo 98915
 
4.5%
por 98915
 
4.5%
Other values (6) 346633
15.7%

Most occurring characters

ValueCountFrequency (%)
1702058
16.8%
o 1188699
11.7%
n 1010015
10.0%
e 899954
8.9%
t 816185
8.1%
m 623009
 
6.1%
s 621582
 
6.1%
i 528094
 
5.2%
0 466698
 
4.6%
r 462121
 
4.6%
Other values (15) 1817265
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10135680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1702058
16.8%
o 1188699
11.7%
n 1010015
10.0%
e 899954
8.9%
t 816185
8.1%
m 623009
 
6.1%
s 621582
 
6.1%
i 528094
 
5.2%
0 466698
 
4.6%
r 462121
 
4.6%
Other values (15) 1817265
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10135680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1702058
16.8%
o 1188699
11.7%
n 1010015
10.0%
e 899954
8.9%
t 816185
8.1%
m 623009
 
6.1%
s 621582
 
6.1%
i 528094
 
5.2%
0 466698
 
4.6%
r 462121
 
4.6%
Other values (15) 1817265
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10135680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1702058
16.8%
o 1188699
11.7%
n 1010015
10.0%
e 899954
8.9%
t 816185
8.1%
m 623009
 
6.1%
s 621582
 
6.1%
i 528094
 
5.2%
0 466698
 
4.6%
r 462121
 
4.6%
Other values (15) 1817265
17.9%

ESTU_DEDICACIONINTERNET
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing16179
Missing (%)3.1%
Memory size37.0 MiB
Entre 1 y 3 horas
155597 
Más de 3 horas
140056 
Entre 30 y 60 minutos
111784 
30 minutos o menos
67477 
No Navega Internet
29214 

Length

Max length21
Median length18
Mean length17.245293
Min length14

Characters and Unicode

Total characters8693835
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Navega Internet
2nd rowNo Navega Internet
3rd rowEntre 1 y 3 horas
4th rowMás de 3 horas
5th rowMás de 3 horas

Common Values

ValueCountFrequency (%)
Entre 1 y 3 horas 155597
29.9%
Más de 3 horas 140056
26.9%
Entre 30 y 60 minutos 111784
21.5%
30 minutos o menos 67477
13.0%
No Navega Internet 29214
 
5.6%
(Missing) 16179
 
3.1%

Length

2024-10-24T11:45:03.262192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:03.308359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 295653
13.1%
horas 295653
13.1%
entre 267381
11.9%
y 267381
11.9%
30 179261
8.0%
minutos 179261
8.0%
1 155597
6.9%
más 140056
6.2%
de 140056
6.2%
60 111784
 
5.0%
Other values (5) 222596
9.9%

Most occurring characters

ValueCountFrequency (%)
1750551
20.1%
s 682447
 
7.8%
o 639082
 
7.4%
r 592248
 
6.8%
n 572547
 
6.6%
e 562556
 
6.5%
t 505070
 
5.8%
3 474914
 
5.5%
a 354081
 
4.1%
h 295653
 
3.4%
Other values (15) 2264686
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8693835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1750551
20.1%
s 682447
 
7.8%
o 639082
 
7.4%
r 592248
 
6.8%
n 572547
 
6.6%
e 562556
 
6.5%
t 505070
 
5.8%
3 474914
 
5.5%
a 354081
 
4.1%
h 295653
 
3.4%
Other values (15) 2264686
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8693835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1750551
20.1%
s 682447
 
7.8%
o 639082
 
7.4%
r 592248
 
6.8%
n 572547
 
6.6%
e 562556
 
6.5%
t 505070
 
5.8%
3 474914
 
5.5%
a 354081
 
4.1%
h 295653
 
3.4%
Other values (15) 2264686
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8693835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1750551
20.1%
s 682447
 
7.8%
o 639082
 
7.4%
r 592248
 
6.8%
n 572547
 
6.6%
e 562556
 
6.5%
t 505070
 
5.8%
3 474914
 
5.5%
a 354081
 
4.1%
h 295653
 
3.4%
Other values (15) 2264686
26.0%

ESTU_HORASSEMANATRABAJA
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing18404
Missing (%)3.5%
Memory size28.9 MiB
0
303662 
Menos de 10 horas
97083 
Entre 11 y 20 horas
51997 
Más de 30 horas
 
27036
Entre 21 y 30 horas
 
22125

Length

Max length19
Median length1
Mean length7.5072893
Min length1

Characters and Unicode

Total characters3767931
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMás de 30 horas
2nd rowEntre 11 y 20 horas
3rd rowEntre 11 y 20 horas
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 303662
58.4%
Menos de 10 horas 97083
 
18.7%
Entre 11 y 20 horas 51997
 
10.0%
Más de 30 horas 27036
 
5.2%
Entre 21 y 30 horas 22125
 
4.3%
(Missing) 18404
 
3.5%

Length

2024-10-24T11:45:03.362862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:03.408155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 303662
25.9%
horas 198241
16.9%
de 124119
10.6%
menos 97083
 
8.3%
10 97083
 
8.3%
entre 74122
 
6.3%
y 74122
 
6.3%
11 51997
 
4.4%
20 51997
 
4.4%
30 49161
 
4.2%
Other values (2) 49161
 
4.2%

Most occurring characters

ValueCountFrequency (%)
668845
17.8%
0 501903
13.3%
s 322360
8.6%
e 295324
7.8%
o 295324
7.8%
r 272363
7.2%
1 223202
 
5.9%
a 198241
 
5.3%
h 198241
 
5.3%
n 171205
 
4.5%
Other values (8) 620923
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3767931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
668845
17.8%
0 501903
13.3%
s 322360
8.6%
e 295324
7.8%
o 295324
7.8%
r 272363
7.2%
1 223202
 
5.9%
a 198241
 
5.3%
h 198241
 
5.3%
n 171205
 
4.5%
Other values (8) 620923
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3767931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
668845
17.8%
0 501903
13.3%
s 322360
8.6%
e 295324
7.8%
o 295324
7.8%
r 272363
7.2%
1 223202
 
5.9%
a 198241
 
5.3%
h 198241
 
5.3%
n 171205
 
4.5%
Other values (8) 620923
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3767931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
668845
17.8%
0 501903
13.3%
s 322360
8.6%
e 295324
7.8%
o 295324
7.8%
r 272363
7.2%
1 223202
 
5.9%
a 198241
 
5.3%
h 198241
 
5.3%
n 171205
 
4.5%
Other values (8) 620923
16.5%

ESTU_TIPOREMUNERACION
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing19104
Missing (%)3.7%
Memory size27.5 MiB
No
334951 
Si, en efectivo
144963 
Si, en especie
 
11610
Si, en efectivo y especie
 
9679

Length

Max length25
Median length2
Mean length6.482128
Min length2

Characters and Unicode

Total characters3248862
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi, en efectivo
2nd rowSi, en efectivo
3rd rowSi, en efectivo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 334951
64.4%
Si, en efectivo 144963
27.9%
Si, en especie 11610
 
2.2%
Si, en efectivo y especie 9679
 
1.9%
(Missing) 19104
 
3.7%

Length

2024-10-24T11:45:03.458397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:03.500081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 334951
39.3%
si 166252
19.5%
en 166252
19.5%
efectivo 154642
18.1%
especie 21289
 
2.5%
y 9679
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 539403
16.6%
o 489593
15.1%
351862
10.8%
i 342183
10.5%
N 334951
10.3%
c 175931
 
5.4%
S 166252
 
5.1%
, 166252
 
5.1%
n 166252
 
5.1%
f 154642
 
4.8%
Other values (5) 361541
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3248862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 539403
16.6%
o 489593
15.1%
351862
10.8%
i 342183
10.5%
N 334951
10.3%
c 175931
 
5.4%
S 166252
 
5.1%
, 166252
 
5.1%
n 166252
 
5.1%
f 154642
 
4.8%
Other values (5) 361541
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3248862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 539403
16.6%
o 489593
15.1%
351862
10.8%
i 342183
10.5%
N 334951
10.3%
c 175931
 
5.4%
S 166252
 
5.1%
, 166252
 
5.1%
n 166252
 
5.1%
f 154642
 
4.8%
Other values (5) 361541
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3248862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 539403
16.6%
o 489593
15.1%
351862
10.8%
i 342183
10.5%
N 334951
10.3%
c 175931
 
5.4%
S 166252
 
5.1%
, 166252
 
5.1%
n 166252
 
5.1%
f 154642
 
4.8%
Other values (5) 361541
11.1%

COLE_CODIGO_ICFES
Real number (ℝ)

Distinct13756
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286320
Minimum83
Maximum752444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:03.548879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile9233
Q159006
median139212
Q3666917
95-th percentile741959
Maximum752444
Range752361
Interquartile range (IQR)607911

Descriptive statistics

Standard deviation288053.44
Coefficient of variation (CV)1.0060542
Kurtosis-1.2767435
Mean286320
Median Absolute Deviation (MAD)106548
Skewness0.72540745
Sum1.489743 × 1011
Variance8.2974786 × 1010
MonotonicityNot monotonic
2024-10-24T11:45:03.606168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
732610 696
 
0.1%
724211 625
 
0.1%
638 540
 
0.1%
726141 477
 
0.1%
164590 444
 
0.1%
67652 405
 
0.1%
727487 397
 
0.1%
23366 383
 
0.1%
740506 356
 
0.1%
147587 352
 
0.1%
Other values (13746) 515632
99.1%
ValueCountFrequency (%)
83 14
 
< 0.1%
91 23
 
< 0.1%
125 52
< 0.1%
141 71
< 0.1%
174 92
< 0.1%
182 68
< 0.1%
190 60
< 0.1%
208 59
< 0.1%
216 26
 
< 0.1%
232 12
 
< 0.1%
ValueCountFrequency (%)
752444 7
 
< 0.1%
752345 70
< 0.1%
752162 15
 
< 0.1%
752154 14
 
< 0.1%
752048 19
 
< 0.1%
752030 1
 
< 0.1%
752014 5
 
< 0.1%
752006 10
 
< 0.1%
751990 36
< 0.1%
751958 10
 
< 0.1%

COLE_COD_DANE_ESTABLECIMIENTO
Real number (ℝ)

HIGH CORRELATION 

Distinct10491
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0578747 × 1011
Minimum1.05001 × 1011
Maximum8.54874 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:03.757062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.05001 × 1011
5-th percentile1.05154 × 1011
Q11.17174 × 1011
median1.73854 × 1011
Q33.05266 × 1011
95-th percentile3.7300101 × 1011
Maximum8.54874 × 1011
Range7.49873 × 1011
Interquartile range (IQR)1.88092 × 1011

Descriptive statistics

Standard deviation9.342611 × 1010
Coefficient of variation (CV)0.45399319
Kurtosis-0.37116651
Mean2.0578747 × 1011
Median Absolute Deviation (MAD)6.2852969 × 1010
Skewness0.68349736
Sum1.0707266 × 1017
Variance8.728438 × 1021
MonotonicityNot monotonic
2024-10-24T11:45:03.814174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.760018001 × 1011831
 
0.2%
1.050010133 × 1011696
 
0.1%
1.050010001 × 1011625
 
0.1%
3.130010067 × 1011571
 
0.1%
4.050010169 × 1011549
 
0.1%
1.056150002 × 1011541
 
0.1%
3.050010245 × 1011540
 
0.1%
1.110010247 × 1011530
 
0.1%
1.051540003 × 1011504
 
0.1%
1.760010058 × 1011477
 
0.1%
Other values (10481) 514443
98.9%
ValueCountFrequency (%)
1.05001 × 101137
 
< 0.1%
1.05001 × 101197
 
< 0.1%
1.050010001 × 1011625
0.1%
1.050010001 × 101168
 
< 0.1%
1.050010001 × 101130
 
< 0.1%
1.050010002 × 101197
 
< 0.1%
1.050010002 × 101163
 
< 0.1%
1.050010003 × 1011287
0.1%
1.050010004 × 101159
 
< 0.1%
1.050010004 × 101193
 
< 0.1%
ValueCountFrequency (%)
8.54874 × 10114
 
< 0.1%
8.470010001 × 101110
 
< 0.1%
8.47001 × 1011104
< 0.1%
6.252690001 × 101162
< 0.1%
6.252690001 × 101128
 
< 0.1%
5.523990001 × 101115
 
< 0.1%
5.25843 × 101114
 
< 0.1%
5.19001 × 1011110
< 0.1%
4.99760001 × 101113
 
< 0.1%
4.990010019 × 101146
< 0.1%
Distinct9665
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size42.4 MiB
2024-10-24T11:45:04.019238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length100
Median length73
Mean length31.059336
Min length4

Characters and Unicode

Total characters16160390
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)< 0.1%

Sample

1st rowI.E. MANUEL RODRIGUEZ TORICES
2nd rowINSTITUTO DE EDUCACIÓN TECNICA INESUR
3rd rowCOORPORACION EDUCATIVA DEL SUR OCCIDENTE COLOMBIANO
4th rowNUEVO INSTITUTO DE APRENDIZAJE SURCOLOMBIANO
5th rowCOL FUNDACION LIC INGLES
ValueCountFrequency (%)
educativa 126056
 
5.4%
de 118091
 
5.0%
institucion 114196
 
4.9%
colegio 89827
 
3.8%
ie 59627
 
2.5%
ied 45744
 
1.9%
i.e 45466
 
1.9%
san 43555
 
1.9%
col 42830
 
1.8%
inst 42157
 
1.8%
Other values (5747) 1625540
69.1%
2024-10-24T11:45:04.303555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1856983
11.5%
A 1666796
10.3%
I 1644968
10.2%
E 1455908
 
9.0%
O 1213661
 
7.5%
N 1019525
 
6.3%
T 921335
 
5.7%
C 915128
 
5.7%
R 785751
 
4.9%
S 756966
 
4.7%
Other values (50) 3923369
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16160390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1856983
11.5%
A 1666796
10.3%
I 1644968
10.2%
E 1455908
 
9.0%
O 1213661
 
7.5%
N 1019525
 
6.3%
T 921335
 
5.7%
C 915128
 
5.7%
R 785751
 
4.9%
S 756966
 
4.7%
Other values (50) 3923369
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16160390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1856983
11.5%
A 1666796
10.3%
I 1644968
10.2%
E 1455908
 
9.0%
O 1213661
 
7.5%
N 1019525
 
6.3%
T 921335
 
5.7%
C 915128
 
5.7%
R 785751
 
4.9%
S 756966
 
4.7%
Other values (50) 3923369
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16160390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1856983
11.5%
A 1666796
10.3%
I 1644968
10.2%
E 1455908
 
9.0%
O 1213661
 
7.5%
N 1019525
 
6.3%
T 921335
 
5.7%
C 915128
 
5.7%
R 785751
 
4.9%
S 756966
 
4.7%
Other values (50) 3923369
24.3%

COLE_GENERO
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.9 MiB
MIXTO
502097 
FEMENINO
 
13754
MASCULINO
 
4456

Length

Max length9
Median length5
Mean length5.1135599
Min length5

Characters and Unicode

Total characters2660621
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIXTO
2nd rowMIXTO
3rd rowMIXTO
4th rowMIXTO
5th rowMIXTO

Common Values

ValueCountFrequency (%)
MIXTO 502097
96.5%
FEMENINO 13754
 
2.6%
MASCULINO 4456
 
0.9%

Length

2024-10-24T11:45:04.380808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:04.423175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mixto 502097
96.5%
femenino 13754
 
2.6%
masculino 4456
 
0.9%

Most occurring characters

ValueCountFrequency (%)
M 520307
19.6%
I 520307
19.6%
O 520307
19.6%
X 502097
18.9%
T 502097
18.9%
N 31964
 
1.2%
E 27508
 
1.0%
F 13754
 
0.5%
A 4456
 
0.2%
S 4456
 
0.2%
Other values (3) 13368
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2660621
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 520307
19.6%
I 520307
19.6%
O 520307
19.6%
X 502097
18.9%
T 502097
18.9%
N 31964
 
1.2%
E 27508
 
1.0%
F 13754
 
0.5%
A 4456
 
0.2%
S 4456
 
0.2%
Other values (3) 13368
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2660621
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 520307
19.6%
I 520307
19.6%
O 520307
19.6%
X 502097
18.9%
T 502097
18.9%
N 31964
 
1.2%
E 27508
 
1.0%
F 13754
 
0.5%
A 4456
 
0.2%
S 4456
 
0.2%
Other values (3) 13368
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2660621
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 520307
19.6%
I 520307
19.6%
O 520307
19.6%
X 502097
18.9%
T 502097
18.9%
N 31964
 
1.2%
E 27508
 
1.0%
F 13754
 
0.5%
A 4456
 
0.2%
S 4456
 
0.2%
Other values (3) 13368
 
0.5%

COLE_NATURALEZA
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.2 MiB
OFICIAL
391688 
NO OFICIAL
128619 

Length

Max length10
Median length7
Mean length7.7415949
Min length7

Characters and Unicode

Total characters4028006
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOFICIAL
2nd rowNO OFICIAL
3rd rowNO OFICIAL
4th rowNO OFICIAL
5th rowNO OFICIAL

Common Values

ValueCountFrequency (%)
OFICIAL 391688
75.3%
NO OFICIAL 128619
 
24.7%

Length

2024-10-24T11:45:04.466719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:04.505224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
oficial 520307
80.2%
no 128619
 
19.8%

Most occurring characters

ValueCountFrequency (%)
I 1040614
25.8%
O 648926
16.1%
F 520307
12.9%
C 520307
12.9%
A 520307
12.9%
L 520307
12.9%
N 128619
 
3.2%
128619
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4028006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1040614
25.8%
O 648926
16.1%
F 520307
12.9%
C 520307
12.9%
A 520307
12.9%
L 520307
12.9%
N 128619
 
3.2%
128619
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4028006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1040614
25.8%
O 648926
16.1%
F 520307
12.9%
C 520307
12.9%
A 520307
12.9%
L 520307
12.9%
N 128619
 
3.2%
128619
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4028006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1040614
25.8%
O 648926
16.1%
F 520307
12.9%
C 520307
12.9%
A 520307
12.9%
L 520307
12.9%
N 128619
 
3.2%
128619
 
3.2%

COLE_CALENDARIO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
A
505748 
B
 
12240
OTRO
 
2319

Length

Max length4
Median length1
Mean length1.013371
Min length1

Characters and Unicode

Total characters527264
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowOTRO
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 505748
97.2%
B 12240
 
2.4%
OTRO 2319
 
0.4%

Length

2024-10-24T11:45:04.547703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:04.587660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 505748
97.2%
b 12240
 
2.4%
otro 2319
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 505748
95.9%
B 12240
 
2.3%
O 4638
 
0.9%
T 2319
 
0.4%
R 2319
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 527264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 505748
95.9%
B 12240
 
2.3%
O 4638
 
0.9%
T 2319
 
0.4%
R 2319
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 527264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 505748
95.9%
B 12240
 
2.3%
O 4638
 
0.9%
T 2319
 
0.4%
R 2319
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 527264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 505748
95.9%
B 12240
 
2.3%
O 4638
 
0.9%
T 2319
 
0.4%
R 2319
 
0.4%

COLE_BILINGUE
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing85622
Missing (%)16.5%
Memory size25.3 MiB
N
426435 
S
 
8250

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters434685
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 426435
82.0%
S 8250
 
1.6%
(Missing) 85622
 
16.5%

Length

2024-10-24T11:45:04.626117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:04.663616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
n 426435
98.1%
s 8250
 
1.9%

Most occurring characters

ValueCountFrequency (%)
N 426435
98.1%
S 8250
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 434685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 426435
98.1%
S 8250
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 434685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 426435
98.1%
S 8250
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 434685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 426435
98.1%
S 8250
 
1.9%

COLE_CARACTER
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing15105
Missing (%)2.9%
Memory size44.1 MiB
ACADÉMICO
271150 
TÉCNICO/ACADÉMICO
170250 
TÉCNICO
56862 
NO APLICA
 
6940

Length

Max length17
Median length9
Mean length11.470845
Min length7

Characters and Unicode

Total characters5795094
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTÉCNICO/ACADÉMICO
2nd rowACADÉMICO
3rd rowTÉCNICO/ACADÉMICO
4th rowACADÉMICO
5th rowTÉCNICO/ACADÉMICO

Common Values

ValueCountFrequency (%)
ACADÉMICO 271150
52.1%
TÉCNICO/ACADÉMICO 170250
32.7%
TÉCNICO 56862
 
10.9%
NO APLICA 6940
 
1.3%
(Missing) 15105
 
2.9%

Length

2024-10-24T11:45:04.706072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:04.745951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
académico 271150
52.9%
técnico/académico 170250
33.2%
técnico 56862
 
11.1%
no 6940
 
1.4%
aplica 6940
 
1.4%

Most occurring characters

ValueCountFrequency (%)
C 1343964
23.2%
A 896680
15.5%
I 675452
11.7%
O 675452
11.7%
É 668512
11.5%
D 441400
 
7.6%
M 441400
 
7.6%
N 234052
 
4.0%
T 227112
 
3.9%
/ 170250
 
2.9%
Other values (3) 20820
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5795094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1343964
23.2%
A 896680
15.5%
I 675452
11.7%
O 675452
11.7%
É 668512
11.5%
D 441400
 
7.6%
M 441400
 
7.6%
N 234052
 
4.0%
T 227112
 
3.9%
/ 170250
 
2.9%
Other values (3) 20820
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5795094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1343964
23.2%
A 896680
15.5%
I 675452
11.7%
O 675452
11.7%
É 668512
11.5%
D 441400
 
7.6%
M 441400
 
7.6%
N 234052
 
4.0%
T 227112
 
3.9%
/ 170250
 
2.9%
Other values (3) 20820
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5795094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1343964
23.2%
A 896680
15.5%
I 675452
11.7%
O 675452
11.7%
É 668512
11.5%
D 441400
 
7.6%
M 441400
 
7.6%
N 234052
 
4.0%
T 227112
 
3.9%
/ 170250
 
2.9%
Other values (3) 20820
 
0.4%

COLE_COD_DANE_SEDE
Real number (ℝ)

HIGH CORRELATION 

Distinct11361
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0618151 × 1011
Minimum1.05001 × 1011
Maximum8.54874 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:04.796377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.05001 × 1011
5-th percentile1.05154 × 1011
Q11.17272 × 1011
median1.76001 × 1011
Q33.05266 × 1011
95-th percentile3.7300101 × 1011
Maximum8.54874 × 1011
Range7.49873 × 1011
Interquartile range (IQR)1.87994 × 1011

Descriptive statistics

Standard deviation9.3930002 × 1010
Coefficient of variation (CV)0.45556947
Kurtosis-0.0085522846
Mean2.0618151 × 1011
Median Absolute Deviation (MAD)6.4999972 × 1010
Skewness0.73042735
Sum1.0727768 × 1017
Variance8.8228452 × 1021
MonotonicityNot monotonic
2024-10-24T11:45:04.852670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.050010133 × 1011696
 
0.1%
1.050010001 × 1011625
 
0.1%
3.130010067 × 1011571
 
0.1%
4.050010169 × 1011549
 
0.1%
1.056150002 × 1011541
 
0.1%
3.050010245 × 1011540
 
0.1%
1.110010247 × 1011530
 
0.1%
1.051540003 × 1011504
 
0.1%
1.760010058 × 1011477
 
0.1%
3.050010174 × 1011452
 
0.1%
Other values (11351) 514822
98.9%
ValueCountFrequency (%)
1.05001 × 101137
 
< 0.1%
1.05001 × 101197
 
< 0.1%
1.050010001 × 1011625
0.1%
1.050010001 × 101168
 
< 0.1%
1.050010001 × 101130
 
< 0.1%
1.050010002 × 101197
 
< 0.1%
1.050010002 × 101145
 
< 0.1%
1.050010002 × 101163
 
< 0.1%
1.050010003 × 1011287
0.1%
1.050010004 × 101159
 
< 0.1%
ValueCountFrequency (%)
8.54874 × 10114
 
< 0.1%
8.470010001 × 101110
 
< 0.1%
8.47001 × 1011104
< 0.1%
8.180011 × 101115
 
< 0.1%
8.180011 × 101129
 
< 0.1%
8.1343 × 101180
< 0.1%
6.252690001 × 101162
< 0.1%
6.252690001 × 101128
 
< 0.1%
5.523990001 × 101115
 
< 0.1%
5.25843 × 101114
 
< 0.1%
Distinct10666
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size43.7 MiB
2024-10-24T11:45:05.034769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length100
Median length79
Mean length32.806476
Min length4

Characters and Unicode

Total characters17069439
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)< 0.1%

Sample

1st rowI.E. MANUEL RODRIGUEZ TORICES
2nd rowINSTITUTO DE EDUCACIÓN TECNICA INESUR - SEDE PRINCIPAL
3rd rowCOORPORACION EDUCATIVA DEL SUR OCCIDENTE COLOMBIANO - SEDE PRINCIPAL
4th rowNUEVO INSTITUTO DE APRENDIZAJE SURCOLOMBIANO - SEDE PRINCIPAL
5th rowCOL FUNDACION LIC INGLES
ValueCountFrequency (%)
sede 147540
 
5.6%
principal 124312
 
4.7%
120407
 
4.6%
de 117343
 
4.5%
educativa 83789
 
3.2%
institucion 69898
 
2.7%
col 67643
 
2.6%
colegio 56857
 
2.2%
educ 52238
 
2.0%
san 43124
 
1.6%
Other values (6146) 1741089
66.3%
2024-10-24T11:45:05.297810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2126459
12.5%
A 1666622
 
9.8%
I 1606307
 
9.4%
E 1584333
 
9.3%
O 1118232
 
6.6%
N 1053207
 
6.2%
C 979390
 
5.7%
S 885639
 
5.2%
R 880523
 
5.2%
L 859048
 
5.0%
Other values (68) 4309679
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17069439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2126459
12.5%
A 1666622
 
9.8%
I 1606307
 
9.4%
E 1584333
 
9.3%
O 1118232
 
6.6%
N 1053207
 
6.2%
C 979390
 
5.7%
S 885639
 
5.2%
R 880523
 
5.2%
L 859048
 
5.0%
Other values (68) 4309679
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17069439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2126459
12.5%
A 1666622
 
9.8%
I 1606307
 
9.4%
E 1584333
 
9.3%
O 1118232
 
6.6%
N 1053207
 
6.2%
C 979390
 
5.7%
S 885639
 
5.2%
R 880523
 
5.2%
L 859048
 
5.0%
Other values (68) 4309679
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17069439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2126459
12.5%
A 1666622
 
9.8%
I 1606307
 
9.4%
E 1584333
 
9.3%
O 1118232
 
6.6%
N 1053207
 
6.2%
C 979390
 
5.7%
S 885639
 
5.2%
R 880523
 
5.2%
L 859048
 
5.0%
Other values (68) 4309679
25.2%

COLE_SEDE_PRINCIPAL
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
S
496259 
N
 
24048

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters520307
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 496259
95.4%
N 24048
 
4.6%

Length

2024-10-24T11:45:05.367369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:05.404174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
s 496259
95.4%
n 24048
 
4.6%

Most occurring characters

ValueCountFrequency (%)
S 496259
95.4%
N 24048
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 496259
95.4%
N 24048
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 496259
95.4%
N 24048
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 496259
95.4%
N 24048
 
4.6%

COLE_AREA_UBICACION
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 MiB
URBANO
435481 
RURAL
84826 

Length

Max length6
Median length6
Mean length5.8369693
Min length5

Characters and Unicode

Total characters3037016
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURBANO
2nd rowURBANO
3rd rowURBANO
4th rowURBANO
5th rowRURAL

Common Values

ValueCountFrequency (%)
URBANO 435481
83.7%
RURAL 84826
 
16.3%

Length

2024-10-24T11:45:05.444000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:05.483142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
urbano 435481
83.7%
rural 84826
 
16.3%

Most occurring characters

ValueCountFrequency (%)
R 605133
19.9%
U 520307
17.1%
A 520307
17.1%
B 435481
14.3%
N 435481
14.3%
O 435481
14.3%
L 84826
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3037016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 605133
19.9%
U 520307
17.1%
A 520307
17.1%
B 435481
14.3%
N 435481
14.3%
O 435481
14.3%
L 84826
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3037016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 605133
19.9%
U 520307
17.1%
A 520307
17.1%
B 435481
14.3%
N 435481
14.3%
O 435481
14.3%
L 84826
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3037016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 605133
19.9%
U 520307
17.1%
A 520307
17.1%
B 435481
14.3%
N 435481
14.3%
O 435481
14.3%
L 84826
 
2.8%

COLE_JORNADA
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.0 MiB
MAÑANA
205624 
UNICA
132384 
COMPLETA
85240 
TARDE
49176 
SABATINA
26316 

Length

Max length8
Median length6
Mean length6.03841
Min length5

Characters and Unicode

Total characters3141827
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOCHE
2nd rowSABATINA
3rd rowMAÑANA
4th rowSABATINA
5th rowCOMPLETA

Common Values

ValueCountFrequency (%)
MAÑANA 205624
39.5%
UNICA 132384
25.4%
COMPLETA 85240
16.4%
TARDE 49176
 
9.5%
SABATINA 26316
 
5.1%
NOCHE 21567
 
4.1%

Length

2024-10-24T11:45:05.526508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:05.572464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mañana 205624
39.5%
unica 132384
25.4%
completa 85240
16.4%
tarde 49176
 
9.5%
sabatina 26316
 
5.1%
noche 21567
 
4.1%

Most occurring characters

ValueCountFrequency (%)
A 962620
30.6%
N 385891
12.3%
M 290864
 
9.3%
C 239191
 
7.6%
Ñ 205624
 
6.5%
T 160732
 
5.1%
I 158700
 
5.1%
E 155983
 
5.0%
U 132384
 
4.2%
O 106807
 
3.4%
Other values (7) 343031
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3141827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 962620
30.6%
N 385891
12.3%
M 290864
 
9.3%
C 239191
 
7.6%
Ñ 205624
 
6.5%
T 160732
 
5.1%
I 158700
 
5.1%
E 155983
 
5.0%
U 132384
 
4.2%
O 106807
 
3.4%
Other values (7) 343031
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3141827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 962620
30.6%
N 385891
12.3%
M 290864
 
9.3%
C 239191
 
7.6%
Ñ 205624
 
6.5%
T 160732
 
5.1%
I 158700
 
5.1%
E 155983
 
5.0%
U 132384
 
4.2%
O 106807
 
3.4%
Other values (7) 343031
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3141827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 962620
30.6%
N 385891
12.3%
M 290864
 
9.3%
C 239191
 
7.6%
Ñ 205624
 
6.5%
T 160732
 
5.1%
I 158700
 
5.1%
E 155983
 
5.0%
U 132384
 
4.2%
O 106807
 
3.4%
Other values (7) 343031
 
10.9%

COLE_COD_MCPIO_UBICACION
Real number (ℝ)

HIGH CORRELATION 

Distinct1111
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32910.716
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:05.626510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median20770
Q354001
95-th percentile76364
Maximum99773
Range94772
Interquartile range (IQR)43000

Descriptive statistics

Standard deviation26544.055
Coefficient of variation (CV)0.80654747
Kurtosis-1.1411508
Mean32910.716
Median Absolute Deviation (MAD)15155
Skewness0.62972513
Sum1.7123676 × 1010
Variance7.0458684 × 108
MonotonicityNot monotonic
2024-10-24T11:45:05.681004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 80406
 
15.5%
5001 28289
 
5.4%
76001 20031
 
3.8%
8001 15227
 
2.9%
13001 12940
 
2.5%
25754 7776
 
1.5%
54001 7774
 
1.5%
73001 7080
 
1.4%
68001 6480
 
1.2%
47001 6203
 
1.2%
Other values (1101) 328101
63.1%
ValueCountFrequency (%)
5001 28289
5.4%
5002 191
 
< 0.1%
5004 18
 
< 0.1%
5021 40
 
< 0.1%
5030 260
 
< 0.1%
5031 254
 
< 0.1%
5034 462
 
0.1%
5036 72
 
< 0.1%
5038 120
 
< 0.1%
5040 209
 
< 0.1%
ValueCountFrequency (%)
99773 173
< 0.1%
99624 43
 
< 0.1%
99524 113
 
< 0.1%
99001 180
< 0.1%
97889 12
 
< 0.1%
97511 7
 
< 0.1%
97001 301
0.1%
95200 24
 
< 0.1%
95025 92
 
< 0.1%
95015 42
 
< 0.1%
Distinct1028
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.7 MiB
2024-10-24T11:45:05.886593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length9.1858326
Min length3

Characters and Unicode

Total characters4779453
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSAN DIEGO
2nd rowIPIALES
3rd rowPOPAYÁN
4th rowMOCOA
5th rowPEREIRA
ValueCountFrequency (%)
bogotá 80406
 
10.9%
d.c 80406
 
10.9%
de 31307
 
4.2%
medellín 28289
 
3.8%
cali 20031
 
2.7%
san 19407
 
2.6%
barranquilla 15227
 
2.1%
cartagena 13133
 
1.8%
indias 12940
 
1.8%
la 10327
 
1.4%
Other values (1018) 427296
57.8%
2024-10-24T11:45:06.165929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 692124
14.5%
O 369256
 
7.7%
E 297642
 
6.2%
L 283612
 
5.9%
C 272247
 
5.7%
N 260822
 
5.5%
I 251530
 
5.3%
R 245397
 
5.1%
D 231047
 
4.8%
T 226204
 
4.7%
Other values (25) 1649572
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4779453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 692124
14.5%
O 369256
 
7.7%
E 297642
 
6.2%
L 283612
 
5.9%
C 272247
 
5.7%
N 260822
 
5.5%
I 251530
 
5.3%
R 245397
 
5.1%
D 231047
 
4.8%
T 226204
 
4.7%
Other values (25) 1649572
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4779453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 692124
14.5%
O 369256
 
7.7%
E 297642
 
6.2%
L 283612
 
5.9%
C 272247
 
5.7%
N 260822
 
5.5%
I 251530
 
5.3%
R 245397
 
5.1%
D 231047
 
4.8%
T 226204
 
4.7%
Other values (25) 1649572
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4779453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 692124
14.5%
O 369256
 
7.7%
E 297642
 
6.2%
L 283612
 
5.9%
C 272247
 
5.7%
N 260822
 
5.5%
I 251530
 
5.3%
R 245397
 
5.1%
D 231047
 
4.8%
T 226204
 
4.7%
Other values (25) 1649572
34.5%

COLE_COD_DEPTO_UBICACION
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.675432
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:06.239646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median20
Q354
95-th percentile76
Maximum99
Range94
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.521581
Coefficient of variation (CV)0.81166735
Kurtosis-1.1384104
Mean32.675432
Median Absolute Deviation (MAD)15
Skewness0.63318146
Sum17001256
Variance703.39428
MonotonicityNot monotonic
2024-10-24T11:45:06.285949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
11 80406
15.5%
5 71170
13.7%
76 41930
 
8.1%
25 35608
 
6.8%
8 29172
 
5.6%
13 24494
 
4.7%
68 24038
 
4.6%
23 19473
 
3.7%
52 15879
 
3.1%
73 15732
 
3.0%
Other values (23) 162405
31.2%
ValueCountFrequency (%)
5 71170
13.7%
8 29172
 
5.6%
11 80406
15.5%
13 24494
 
4.7%
15 15355
 
3.0%
17 10100
 
1.9%
18 3924
 
0.8%
19 13217
 
2.5%
20 12695
 
2.4%
23 19473
 
3.7%
ValueCountFrequency (%)
99 509
 
0.1%
97 320
 
0.1%
95 808
 
0.2%
94 283
 
0.1%
91 630
 
0.1%
88 590
 
0.1%
86 3673
 
0.7%
85 5484
 
1.1%
81 2993
 
0.6%
76 41930
8.1%

COLE_DEPTO_UBICACION
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.2 MiB
BOGOTÁ
80406 
ANTIOQUIA
71170 
VALLE
41930 
CUNDINAMARCA
35608 
ATLANTICO
29172 
Other values (28)
262021 

Length

Max length15
Median length12
Mean length7.507977
Min length4

Characters and Unicode

Total characters3906453
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCESAR
2nd rowNARIÑO
3rd rowCAUCA
4th rowPUTUMAYO
5th rowRISARALDA

Common Values

ValueCountFrequency (%)
BOGOTÁ 80406
15.5%
ANTIOQUIA 71170
13.7%
VALLE 41930
 
8.1%
CUNDINAMARCA 35608
 
6.8%
ATLANTICO 29172
 
5.6%
BOLIVAR 24494
 
4.7%
SANTANDER 24038
 
4.6%
CORDOBA 19473
 
3.7%
NARIÑO 15879
 
3.1%
TOLIMA 15732
 
3.0%
Other values (23) 162405
31.2%

Length

2024-10-24T11:45:06.335300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogotá 80406
14.8%
antioquia 71170
13.1%
valle 41930
 
7.7%
santander 38610
 
7.1%
cundinamarca 35608
 
6.6%
atlantico 29172
 
5.4%
bolivar 24494
 
4.5%
cordoba 19473
 
3.6%
nariño 15879
 
2.9%
tolima 15732
 
2.9%
Other values (25) 170964
31.5%

Most occurring characters

ValueCountFrequency (%)
A 758518
19.4%
O 404905
10.4%
N 308063
 
7.9%
I 306553
 
7.8%
T 298212
 
7.6%
C 216155
 
5.5%
L 208468
 
5.3%
R 208455
 
5.3%
U 171901
 
4.4%
E 156301
 
4.0%
Other values (15) 868922
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3906453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 758518
19.4%
O 404905
10.4%
N 308063
 
7.9%
I 306553
 
7.8%
T 298212
 
7.6%
C 216155
 
5.5%
L 208468
 
5.3%
R 208455
 
5.3%
U 171901
 
4.4%
E 156301
 
4.0%
Other values (15) 868922
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3906453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 758518
19.4%
O 404905
10.4%
N 308063
 
7.9%
I 306553
 
7.8%
T 298212
 
7.6%
C 216155
 
5.5%
L 208468
 
5.3%
R 208455
 
5.3%
U 171901
 
4.4%
E 156301
 
4.0%
Other values (15) 868922
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3906453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 758518
19.4%
O 404905
10.4%
N 308063
 
7.9%
I 306553
 
7.8%
T 298212
 
7.6%
C 216155
 
5.5%
L 208468
 
5.3%
R 208455
 
5.3%
U 171901
 
4.4%
E 156301
 
4.0%
Other values (15) 868922
22.2%

ESTU_PRIVADO_LIBERTAD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
N
520153 
S
 
154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters520307
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 520153
> 99.9%
S 154
 
< 0.1%

Length

2024-10-24T11:45:06.379686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:06.415401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
n 520153
> 99.9%
s 154
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 520153
> 99.9%
S 154
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 520153
> 99.9%
S 154
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 520153
> 99.9%
S 154
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 520153
> 99.9%
S 154
 
< 0.1%

ESTU_COD_MCPIO_PRESENTACION
Real number (ℝ)

HIGH CORRELATION 

Distinct501
Distinct (%)0.1%
Missing208
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean32917.955
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:06.458748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median20400
Q354001
95-th percentile76364
Maximum99773
Range94772
Interquartile range (IQR)43000

Descriptive statistics

Standard deviation26619.865
Coefficient of variation (CV)0.80867312
Kurtosis-1.145261
Mean32917.955
Median Absolute Deviation (MAD)14821
Skewness0.62858207
Sum1.7120595 × 1010
Variance7.0861722 × 108
MonotonicityNot monotonic
2024-10-24T11:45:06.509825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 81530
 
15.7%
5001 27286
 
5.2%
76001 20485
 
3.9%
8001 17112
 
3.3%
13001 14026
 
2.7%
54001 8643
 
1.7%
73001 7750
 
1.5%
25754 7490
 
1.4%
68001 7197
 
1.4%
50001 6253
 
1.2%
Other values (491) 322327
61.9%
ValueCountFrequency (%)
5001 27286
5.2%
5002 195
 
< 0.1%
5030 700
 
0.1%
5031 427
 
0.1%
5034 712
 
0.1%
5042 922
 
0.2%
5045 1905
 
0.4%
5051 580
 
0.1%
5079 702
 
0.1%
5088 5130
 
1.0%
ValueCountFrequency (%)
99773 152
< 0.1%
99624 52
 
< 0.1%
99524 119
< 0.1%
99001 184
< 0.1%
97889 12
 
< 0.1%
97666 18
 
< 0.1%
97511 7
 
< 0.1%
97161 39
 
< 0.1%
97001 245
< 0.1%
95200 24
 
< 0.1%
Distinct485
Distinct (%)0.1%
Missing208
Missing (%)< 0.1%
Memory size34.9 MiB
2024-10-24T11:45:06.701118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length9.2542266
Min length3

Characters and Unicode

Total characters4813114
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowVALLEDUPAR
2nd rowIPIALES
3rd rowPOPAYÁN
4th rowMOCOA
5th rowPEREIRA
ValueCountFrequency (%)
bogotá 81530
 
11.0%
d.c 81530
 
11.0%
de 35103
 
4.7%
medellín 27286
 
3.7%
cali 20485
 
2.8%
san 18409
 
2.5%
barranquilla 17112
 
2.3%
cartagena 14224
 
1.9%
indias 14026
 
1.9%
la 11249
 
1.5%
Other values (513) 421490
56.8%
2024-10-24T11:45:06.960522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 709238
14.7%
O 362468
 
7.5%
E 288613
 
6.0%
L 275830
 
5.7%
C 273785
 
5.7%
N 263795
 
5.5%
I 258307
 
5.4%
R 246069
 
5.1%
D 233649
 
4.9%
T 224235
 
4.7%
Other values (24) 1677125
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4813114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 709238
14.7%
O 362468
 
7.5%
E 288613
 
6.0%
L 275830
 
5.7%
C 273785
 
5.7%
N 263795
 
5.5%
I 258307
 
5.4%
R 246069
 
5.1%
D 233649
 
4.9%
T 224235
 
4.7%
Other values (24) 1677125
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4813114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 709238
14.7%
O 362468
 
7.5%
E 288613
 
6.0%
L 275830
 
5.7%
C 273785
 
5.7%
N 263795
 
5.5%
I 258307
 
5.4%
R 246069
 
5.1%
D 233649
 
4.9%
T 224235
 
4.7%
Other values (24) 1677125
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4813114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 709238
14.7%
O 362468
 
7.5%
E 288613
 
6.0%
L 275830
 
5.7%
C 273785
 
5.7%
N 263795
 
5.5%
I 258307
 
5.4%
R 246069
 
5.1%
D 233649
 
4.9%
T 224235
 
4.7%
Other values (24) 1677125
34.8%

ESTU_DEPTO_PRESENTACION
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)< 0.1%
Missing208
Missing (%)< 0.1%
Memory size30.3 MiB
BOGOTÁ
81530 
ANTIOQUIA
71272 
VALLE
42228 
CUNDINAMARCA
33832 
ATLANTICO
29796 
Other values (28)
261441 

Length

Max length15
Median length12
Mean length7.4936599
Min length4

Characters and Unicode

Total characters3897445
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCESAR
2nd rowNARIÑO
3rd rowCAUCA
4th rowPUTUMAYO
5th rowRISARALDA

Common Values

ValueCountFrequency (%)
BOGOTÁ 81530
15.7%
ANTIOQUIA 71272
13.7%
VALLE 42228
 
8.1%
CUNDINAMARCA 33832
 
6.5%
ATLANTICO 29796
 
5.7%
SANTANDER 23993
 
4.6%
BOLIVAR 23325
 
4.5%
CORDOBA 19362
 
3.7%
NARIÑO 15918
 
3.1%
TOLIMA 15566
 
3.0%
Other values (23) 163277
31.4%

Length

2024-10-24T11:45:07.036823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogotá 81530
15.0%
antioquia 71272
13.1%
valle 42228
 
7.8%
santander 38844
 
7.1%
cundinamarca 33832
 
6.2%
atlantico 29796
 
5.5%
bolivar 23325
 
4.3%
cordoba 19362
 
3.6%
nariño 15918
 
2.9%
tolima 15566
 
2.9%
Other values (25) 171867
31.6%

Most occurring characters

ValueCountFrequency (%)
A 755047
19.4%
O 406701
10.4%
N 306075
 
7.9%
I 304204
 
7.8%
T 301075
 
7.7%
C 213554
 
5.5%
L 208508
 
5.3%
R 206071
 
5.3%
U 170539
 
4.4%
E 157456
 
4.0%
Other values (15) 868215
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3897445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 755047
19.4%
O 406701
10.4%
N 306075
 
7.9%
I 304204
 
7.8%
T 301075
 
7.7%
C 213554
 
5.5%
L 208508
 
5.3%
R 206071
 
5.3%
U 170539
 
4.4%
E 157456
 
4.0%
Other values (15) 868215
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3897445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 755047
19.4%
O 406701
10.4%
N 306075
 
7.9%
I 304204
 
7.8%
T 301075
 
7.7%
C 213554
 
5.5%
L 208508
 
5.3%
R 206071
 
5.3%
U 170539
 
4.4%
E 157456
 
4.0%
Other values (15) 868215
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3897445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 755047
19.4%
O 406701
10.4%
N 306075
 
7.9%
I 304204
 
7.8%
T 301075
 
7.7%
C 213554
 
5.5%
L 208508
 
5.3%
R 206071
 
5.3%
U 170539
 
4.4%
E 157456
 
4.0%
Other values (15) 868215
22.3%

ESTU_COD_DEPTO_PRESENTACION
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)< 0.1%
Missing208
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean32.700511
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:07.084124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median20
Q354
95-th percentile76
Maximum99
Range94
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.595928
Coefficient of variation (CV)0.81331841
Kurtosis-1.1431172
Mean32.700511
Median Absolute Deviation (MAD)15
Skewness0.63151454
Sum17007503
Variance707.34336
MonotonicityNot monotonic
2024-10-24T11:45:07.230898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
11 81530
15.7%
5 71272
13.7%
76 42228
 
8.1%
25 33832
 
6.5%
8 29796
 
5.7%
68 23993
 
4.6%
13 23325
 
4.5%
23 19362
 
3.7%
52 15918
 
3.1%
73 15566
 
3.0%
Other values (23) 163277
31.4%
ValueCountFrequency (%)
5 71272
13.7%
8 29796
 
5.7%
11 81530
15.7%
13 23325
 
4.5%
15 15498
 
3.0%
17 10421
 
2.0%
18 3979
 
0.8%
19 12985
 
2.5%
20 12893
 
2.5%
23 19362
 
3.7%
ValueCountFrequency (%)
99 507
 
0.1%
97 321
 
0.1%
95 894
 
0.2%
94 282
 
0.1%
91 631
 
0.1%
88 602
 
0.1%
86 3752
 
0.7%
85 5647
 
1.1%
81 3123
 
0.6%
76 42228
8.1%

PUNT_LECTURA_CRITICA
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.368763
Minimum0
Maximum100
Zeros201
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:07.282931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q145
median53
Q360
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.275937
Coefficient of variation (CV)0.19622264
Kurtosis0.04569837
Mean52.368763
Median Absolute Deviation (MAD)7
Skewness0.025436811
Sum27247834
Variance105.59488
MonotonicityNot monotonic
2024-10-24T11:45:07.338696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 19342
 
3.7%
53 19040
 
3.7%
55 19005
 
3.7%
56 18824
 
3.6%
52 18795
 
3.6%
51 18599
 
3.6%
57 18265
 
3.5%
50 17969
 
3.5%
58 17693
 
3.4%
49 17417
 
3.3%
Other values (54) 335358
64.5%
ValueCountFrequency (%)
0 201
 
< 0.1%
22 13
 
< 0.1%
23 31
 
< 0.1%
24 99
 
< 0.1%
25 162
 
< 0.1%
26 327
 
0.1%
27 552
 
0.1%
28 794
0.2%
29 1228
0.2%
30 1771
0.3%
ValueCountFrequency (%)
100 479
 
0.1%
83 3
 
< 0.1%
82 12
 
< 0.1%
81 61
 
< 0.1%
80 144
 
< 0.1%
79 484
 
0.1%
78 606
 
0.1%
77 897
0.2%
76 1339
0.3%
75 1717
0.3%

PERCENTIL_LECTURA_CRITICA
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.196332
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:07.393342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.908654
Coefficient of variation (CV)0.57591168
Kurtosis-1.2042593
Mean50.196332
Median Absolute Deviation (MAD)25
Skewness0.012382621
Sum26117503
Variance835.71026
MonotonicityNot monotonic
2024-10-24T11:45:07.449376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 5379
 
1.0%
4 5362
 
1.0%
6 5361
 
1.0%
9 5358
 
1.0%
8 5338
 
1.0%
5 5333
 
1.0%
3 5331
 
1.0%
11 5327
 
1.0%
12 5323
 
1.0%
10 5317
 
1.0%
Other values (90) 466878
89.7%
ValueCountFrequency (%)
1 5173
1.0%
2 5254
1.0%
3 5331
1.0%
4 5362
1.0%
5 5333
1.0%
6 5361
1.0%
7 5379
1.0%
8 5338
1.0%
9 5358
1.0%
10 5317
1.0%
ValueCountFrequency (%)
100 5132
1.0%
99 5142
1.0%
98 5138
1.0%
97 5137
1.0%
96 5137
1.0%
95 5127
1.0%
94 5153
1.0%
93 5148
1.0%
92 5151
1.0%
91 5140
1.0%

DESEMP_LECTURA_CRITICA
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
3
245067 
2
196610 
4
53190 
1
25440 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters520307
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 245067
47.1%
2 196610
37.8%
4 53190
 
10.2%
1 25440
 
4.9%

Length

2024-10-24T11:45:07.499741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:07.539380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 245067
47.1%
2 196610
37.8%
4 53190
 
10.2%
1 25440
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 245067
47.1%
2 196610
37.8%
4 53190
 
10.2%
1 25440
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 245067
47.1%
2 196610
37.8%
4 53190
 
10.2%
1 25440
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 245067
47.1%
2 196610
37.8%
4 53190
 
10.2%
1 25440
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 245067
47.1%
2 196610
37.8%
4 53190
 
10.2%
1 25440
 
4.9%

PUNT_MATEMATICAS
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.245297
Minimum0
Maximum100
Zeros186
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:07.590430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q143
median51
Q359
95-th percentile70
Maximum100
Range100
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.778498
Coefficient of variation (CV)0.22984544
Kurtosis0.25153296
Mean51.245297
Median Absolute Deviation (MAD)8
Skewness0.15304561
Sum26663287
Variance138.73302
MonotonicityNot monotonic
2024-10-24T11:45:07.647542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 16831
 
3.2%
51 16633
 
3.2%
55 16624
 
3.2%
52 16623
 
3.2%
54 16417
 
3.2%
50 16372
 
3.1%
56 16209
 
3.1%
49 16206
 
3.1%
57 15895
 
3.1%
48 15587
 
3.0%
Other values (62) 356910
68.6%
ValueCountFrequency (%)
0 186
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
17 30
 
< 0.1%
18 85
 
< 0.1%
19 160
 
< 0.1%
20 274
 
0.1%
21 403
0.1%
22 586
0.1%
23 741
0.1%
ValueCountFrequency (%)
100 1481
0.3%
84 14
 
< 0.1%
83 71
 
< 0.1%
82 229
 
< 0.1%
81 400
 
0.1%
80 666
 
0.1%
79 1097
0.2%
78 1306
0.3%
77 1508
0.3%
76 1907
0.4%

PERCENTIL_MATEMATICAS
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.230437
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:07.704831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.874362
Coefficient of variation (CV)0.57483796
Kurtosis-1.2038166
Mean50.230437
Median Absolute Deviation (MAD)25
Skewness0.014132744
Sum26135248
Variance833.72878
MonotonicityNot monotonic
2024-10-24T11:45:07.760787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 5358
 
1.0%
13 5331
 
1.0%
7 5330
 
1.0%
9 5329
 
1.0%
11 5328
 
1.0%
14 5325
 
1.0%
6 5319
 
1.0%
16 5319
 
1.0%
8 5318
 
1.0%
17 5304
 
1.0%
Other values (90) 467046
89.8%
ValueCountFrequency (%)
1 4891
0.9%
2 5173
1.0%
3 5268
1.0%
4 5253
1.0%
5 5293
1.0%
6 5319
1.0%
7 5330
1.0%
8 5318
1.0%
9 5329
1.0%
10 5358
1.0%
ValueCountFrequency (%)
100 5140
1.0%
99 5143
1.0%
98 5146
1.0%
97 5129
1.0%
96 5153
1.0%
95 5138
1.0%
94 5152
1.0%
93 5142
1.0%
92 5144
1.0%
91 5145
1.0%

DESEMP_MATEMATICAS
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
3
246695 
2
201363 
1
46697 
4
25552 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters520307
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 246695
47.4%
2 201363
38.7%
1 46697
 
9.0%
4 25552
 
4.9%

Length

2024-10-24T11:45:07.811585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:07.851720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 246695
47.4%
2 201363
38.7%
1 46697
 
9.0%
4 25552
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 246695
47.4%
2 201363
38.7%
1 46697
 
9.0%
4 25552
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 246695
47.4%
2 201363
38.7%
1 46697
 
9.0%
4 25552
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 246695
47.4%
2 201363
38.7%
1 46697
 
9.0%
4 25552
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 246695
47.4%
2 201363
38.7%
1 46697
 
9.0%
4 25552
 
4.9%

PUNT_C_NATURALES
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.430775
Minimum0
Maximum100
Zeros395
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:07.902634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q140
median48
Q356
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.644018
Coefficient of variation (CV)0.21977799
Kurtosis0.10785544
Mean48.430775
Median Absolute Deviation (MAD)8
Skewness0.26594462
Sum25198871
Variance113.29512
MonotonicityNot monotonic
2024-10-24T11:45:07.959696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 17872
 
3.4%
46 17678
 
3.4%
48 17598
 
3.4%
50 17474
 
3.4%
44 17460
 
3.4%
45 17368
 
3.3%
43 17362
 
3.3%
49 17182
 
3.3%
51 17070
 
3.3%
42 16972
 
3.3%
Other values (54) 346271
66.6%
ValueCountFrequency (%)
0 395
 
0.1%
21 1
 
< 0.1%
22 3
 
< 0.1%
23 9
 
< 0.1%
24 110
 
< 0.1%
25 443
 
0.1%
26 863
 
0.2%
27 1392
0.3%
28 2231
0.4%
29 3320
0.6%
ValueCountFrequency (%)
100 412
 
0.1%
82 6
 
< 0.1%
81 36
 
< 0.1%
80 153
 
< 0.1%
79 220
 
< 0.1%
78 449
 
0.1%
77 645
0.1%
76 867
0.2%
75 1150
0.2%
74 1377
0.3%

PERCENTIL_C_NATURALES
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.272291
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:08.015632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.865075
Coefficient of variation (CV)0.57417465
Kurtosis-1.2007688
Mean50.272291
Median Absolute Deviation (MAD)25
Skewness0.010889337
Sum26157025
Variance833.19257
MonotonicityNot monotonic
2024-10-24T11:45:08.072875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 5290
 
1.0%
19 5287
 
1.0%
17 5283
 
1.0%
5 5275
 
1.0%
14 5272
 
1.0%
7 5271
 
1.0%
24 5269
 
1.0%
16 5269
 
1.0%
18 5266
 
1.0%
23 5264
 
1.0%
Other values (90) 467561
89.9%
ValueCountFrequency (%)
1 5236
1.0%
2 5230
1.0%
3 5246
1.0%
4 5258
1.0%
5 5275
1.0%
6 5219
1.0%
7 5271
1.0%
8 5259
1.0%
9 5244
1.0%
10 5262
1.0%
ValueCountFrequency (%)
100 5136
1.0%
99 5158
1.0%
98 5145
1.0%
97 5141
1.0%
96 5140
1.0%
95 5161
1.0%
94 5141
1.0%
93 5151
1.0%
92 5152
1.0%
91 5156
1.0%

DESEMP_C_NATURALES
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
2
253568 
1
131969 
3
122989 
4
 
11781

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters520307
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 253568
48.7%
1 131969
25.4%
3 122989
23.6%
4 11781
 
2.3%

Length

2024-10-24T11:45:08.122769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:08.163089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 253568
48.7%
1 131969
25.4%
3 122989
23.6%
4 11781
 
2.3%

Most occurring characters

ValueCountFrequency (%)
2 253568
48.7%
1 131969
25.4%
3 122989
23.6%
4 11781
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 253568
48.7%
1 131969
25.4%
3 122989
23.6%
4 11781
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 253568
48.7%
1 131969
25.4%
3 122989
23.6%
4 11781
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 253568
48.7%
1 131969
25.4%
3 122989
23.6%
4 11781
 
2.3%

PUNT_SOCIALES_CIUDADANAS
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.44127
Minimum0
Maximum100
Zeros192
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:08.213411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q139
median48
Q357
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.086911
Coefficient of variation (CV)0.2495168
Kurtosis-0.12984835
Mean48.44127
Median Absolute Deviation (MAD)9
Skewness0.30341517
Sum25204332
Variance146.09341
MonotonicityNot monotonic
2024-10-24T11:45:08.269707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 15382
 
3.0%
41 15317
 
2.9%
40 15283
 
2.9%
42 15268
 
2.9%
43 15216
 
2.9%
38 15021
 
2.9%
44 14876
 
2.9%
45 14685
 
2.8%
46 14500
 
2.8%
47 14351
 
2.8%
Other values (58) 370408
71.2%
ValueCountFrequency (%)
0 192
 
< 0.1%
18 4
 
< 0.1%
19 6
 
< 0.1%
20 111
 
< 0.1%
21 275
 
0.1%
22 453
 
0.1%
23 741
 
0.1%
24 1174
0.2%
25 1791
0.3%
26 2423
0.5%
ValueCountFrequency (%)
100 1019
0.2%
83 25
 
< 0.1%
82 37
 
< 0.1%
81 60
 
< 0.1%
80 78
 
< 0.1%
79 475
 
0.1%
78 1046
0.2%
77 1198
0.2%
76 1468
0.3%
75 1902
0.4%

PERCENTIL_SOCIALES_CIUDADANAS
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.239732
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:08.326595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.889732
Coefficient of variation (CV)0.57503755
Kurtosis-1.2015685
Mean50.239732
Median Absolute Deviation (MAD)25
Skewness0.010166637
Sum26140084
Variance834.61664
MonotonicityNot monotonic
2024-10-24T11:45:08.382579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5364
 
1.0%
16 5311
 
1.0%
2 5305
 
1.0%
7 5303
 
1.0%
6 5301
 
1.0%
3 5300
 
1.0%
13 5293
 
1.0%
5 5291
 
1.0%
4 5290
 
1.0%
8 5283
 
1.0%
Other values (90) 467266
89.8%
ValueCountFrequency (%)
1 5364
1.0%
2 5305
1.0%
3 5300
1.0%
4 5290
1.0%
5 5291
1.0%
6 5301
1.0%
7 5303
1.0%
8 5283
1.0%
9 5277
1.0%
10 5263
1.0%
ValueCountFrequency (%)
100 5139
1.0%
99 5129
1.0%
98 5151
1.0%
97 5139
1.0%
96 5138
1.0%
95 5133
1.0%
94 5144
1.0%
93 5149
1.0%
92 5155
1.0%
91 5146
1.0%

DESEMP_SOCIALES_CIUDADANAS
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
2
214596 
1
153154 
3
133855 
4
 
18702

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters520307
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 214596
41.2%
1 153154
29.4%
3 133855
25.7%
4 18702
 
3.6%

Length

2024-10-24T11:45:08.432477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:08.472584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 214596
41.2%
1 153154
29.4%
3 133855
25.7%
4 18702
 
3.6%

Most occurring characters

ValueCountFrequency (%)
2 214596
41.2%
1 153154
29.4%
3 133855
25.7%
4 18702
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 214596
41.2%
1 153154
29.4%
3 133855
25.7%
4 18702
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 214596
41.2%
1 153154
29.4%
3 133855
25.7%
4 18702
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 214596
41.2%
1 153154
29.4%
3 133855
25.7%
4 18702
 
3.6%

PUNT_INGLES
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)< 0.1%
Missing370
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean47.418543
Minimum0
Maximum100
Zeros1296
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:08.523152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q139
median45
Q353
95-th percentile72
Maximum100
Range100
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.959334
Coefficient of variation (CV)0.25220797
Kurtosis1.8974633
Mean47.418543
Median Absolute Deviation (MAD)7
Skewness0.9555242
Sum24654655
Variance143.02568
MonotonicityNot monotonic
2024-10-24T11:45:08.580806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 21897
 
4.2%
43 21689
 
4.2%
41 21482
 
4.1%
44 21415
 
4.1%
45 21170
 
4.1%
40 20928
 
4.0%
46 20264
 
3.9%
39 20054
 
3.9%
47 19484
 
3.7%
38 19237
 
3.7%
Other values (60) 312317
60.0%
ValueCountFrequency (%)
0 1296
0.2%
21 3
 
< 0.1%
22 6
 
< 0.1%
23 18
 
< 0.1%
24 24
 
< 0.1%
25 30
 
< 0.1%
26 40
 
< 0.1%
27 44
 
< 0.1%
28 1016
0.2%
29 2386
0.5%
ValueCountFrequency (%)
100 1924
0.4%
88 15
 
< 0.1%
87 106
 
< 0.1%
86 376
 
0.1%
85 533
 
0.1%
84 738
 
0.1%
83 874
0.2%
82 946
0.2%
81 1236
0.2%
80 1522
0.3%

PERCENTIL_INGLES
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.132697
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:08.635775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.935389
Coefficient of variation (CV)0.577176
Kurtosis-1.2022729
Mean50.132697
Median Absolute Deviation (MAD)25
Skewness0.014245635
Sum26084393
Variance837.25675
MonotonicityNot monotonic
2024-10-24T11:45:08.692021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 5878
 
1.1%
1 5628
 
1.1%
4 5369
 
1.0%
3 5369
 
1.0%
5 5364
 
1.0%
2 5351
 
1.0%
6 5337
 
1.0%
8 5336
 
1.0%
10 5329
 
1.0%
13 5319
 
1.0%
Other values (90) 466027
89.6%
ValueCountFrequency (%)
1 5628
1.1%
2 5351
1.0%
3 5369
1.0%
4 5369
1.0%
5 5364
1.0%
6 5337
1.0%
7 5318
1.0%
8 5336
1.0%
9 5297
1.0%
10 5329
1.0%
ValueCountFrequency (%)
100 5878
1.1%
99 4963
1.0%
98 4983
1.0%
97 4981
1.0%
96 4984
1.0%
95 4974
1.0%
94 5153
1.0%
93 5084
1.0%
92 5180
1.0%
91 5111
1.0%

DESEMP_INGLES
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.3 MiB
A-
305111 
A1
129008 
A2
45595 
B1
30749 
B+
 
9844

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1040614
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowA-
3rd rowA-
4th rowA-
5th rowB+

Common Values

ValueCountFrequency (%)
A- 305111
58.6%
A1 129008
24.8%
A2 45595
 
8.8%
B1 30749
 
5.9%
B+ 9844
 
1.9%

Length

2024-10-24T11:45:08.744781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:08.789640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 305111
58.6%
a1 129008
24.8%
a2 45595
 
8.8%
b1 30749
 
5.9%
b 9844
 
1.9%

Most occurring characters

ValueCountFrequency (%)
A 479714
46.1%
- 305111
29.3%
1 159757
 
15.4%
2 45595
 
4.4%
B 40593
 
3.9%
+ 9844
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1040614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 479714
46.1%
- 305111
29.3%
1 159757
 
15.4%
2 45595
 
4.4%
B 40593
 
3.9%
+ 9844
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1040614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 479714
46.1%
- 305111
29.3%
1 159757
 
15.4%
2 45595
 
4.4%
B 40593
 
3.9%
+ 9844
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1040614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 479714
46.1%
- 305111
29.3%
1 159757
 
15.4%
2 45595
 
4.4%
B 40593
 
3.9%
+ 9844
 
0.9%

PUNT_GLOBAL
Real number (ℝ)

HIGH CORRELATION 

Distinct434
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.56596
Minimum0
Maximum500
Zeros81
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:08.944654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile176
Q1212
median246
Q3283
95-th percentile336
Maximum500
Range500
Interquartile range (IQR)71

Descriptive statistics

Standard deviation49.568905
Coefficient of variation (CV)0.19862045
Kurtosis-0.12307519
Mean249.56596
Median Absolute Deviation (MAD)36
Skewness0.33260177
Sum1.2985092 × 108
Variance2457.0763
MonotonicityNot monotonic
2024-10-24T11:45:09.000479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 4518
 
0.9%
235 4494
 
0.9%
225 4482
 
0.9%
223 4466
 
0.9%
242 4454
 
0.9%
228 4436
 
0.9%
237 4435
 
0.9%
227 4418
 
0.8%
247 4416
 
0.8%
245 4398
 
0.8%
Other values (424) 475790
91.4%
ValueCountFrequency (%)
0 81
< 0.1%
11 2
 
< 0.1%
12 3
 
< 0.1%
14 1
 
< 0.1%
20 2
 
< 0.1%
21 4
 
< 0.1%
22 2
 
< 0.1%
23 7
 
< 0.1%
24 8
 
< 0.1%
25 7
 
< 0.1%
ValueCountFrequency (%)
500 2
 
< 0.1%
492 4
< 0.1%
491 1
 
< 0.1%
479 1
 
< 0.1%
477 1
 
< 0.1%
476 1
 
< 0.1%
475 3
< 0.1%
473 4
< 0.1%
472 5
< 0.1%
471 2
 
< 0.1%

PERCENTIL_GLOBAL
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)< 0.1%
Missing36
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean50.102912
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:09.055000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.903357
Coefficient of variation (CV)0.57687978
Kurtosis-1.2028794
Mean50.102912
Median Absolute Deviation (MAD)25
Skewness0.015828757
Sum26067092
Variance835.40403
MonotonicityNot monotonic
2024-10-24T11:45:09.112655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 6141
 
1.2%
46 6087
 
1.2%
73 6084
 
1.2%
81 6049
 
1.2%
34 5985
 
1.2%
36 5984
 
1.2%
44 5972
 
1.1%
38 5963
 
1.1%
40 5952
 
1.1%
50 5947
 
1.1%
Other values (90) 460107
88.4%
ValueCountFrequency (%)
1 5352
1.0%
2 5271
1.0%
3 5487
1.1%
4 5377
1.0%
5 5522
1.1%
6 5525
1.1%
7 5183
1.0%
8 5406
1.0%
9 4983
1.0%
10 5048
1.0%
ValueCountFrequency (%)
100 5059
1.0%
99 5024
1.0%
98 5068
1.0%
97 5349
1.0%
96 5147
1.0%
95 4755
0.9%
94 5309
1.0%
93 5388
1.0%
92 4812
0.9%
91 5184
1.0%

ESTU_ESTADOINVESTIGACION
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.3 MiB
PUBLICAR
520047 
VALIDEZ OFICINA JURÍDICA
 
260

Length

Max length24
Median length8
Mean length8.0079953
Min length8

Characters and Unicode

Total characters4166616
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVALIDEZ OFICINA JURÍDICA
2nd rowPUBLICAR
3rd rowPUBLICAR
4th rowPUBLICAR
5th rowPUBLICAR

Common Values

ValueCountFrequency (%)
PUBLICAR 520047
> 99.9%
VALIDEZ OFICINA JURÍDICA 260
 
< 0.1%

Length

2024-10-24T11:45:09.168467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:09.210791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
publicar 520047
99.9%
validez 260
 
< 0.1%
oficina 260
 
< 0.1%
jurídica 260
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 521087
12.5%
A 520827
12.5%
C 520567
12.5%
U 520307
12.5%
L 520307
12.5%
R 520307
12.5%
P 520047
12.5%
B 520047
12.5%
520
 
< 0.1%
D 520
 
< 0.1%
Other values (8) 2080
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4166616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 521087
12.5%
A 520827
12.5%
C 520567
12.5%
U 520307
12.5%
L 520307
12.5%
R 520307
12.5%
P 520047
12.5%
B 520047
12.5%
520
 
< 0.1%
D 520
 
< 0.1%
Other values (8) 2080
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4166616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 521087
12.5%
A 520827
12.5%
C 520567
12.5%
U 520307
12.5%
L 520307
12.5%
R 520307
12.5%
P 520047
12.5%
B 520047
12.5%
520
 
< 0.1%
D 520
 
< 0.1%
Other values (8) 2080
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4166616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 521087
12.5%
A 520827
12.5%
C 520567
12.5%
U 520307
12.5%
L 520307
12.5%
R 520307
12.5%
P 520047
12.5%
B 520047
12.5%
520
 
< 0.1%
D 520
 
< 0.1%
Other values (8) 2080
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.6 MiB
NO
269228 
GENERACION E - GRATUIDAD
246190 
GENERACION E - EXCELENCIA NACIONAL
 
4701
GENERACION E - EXCELENCIA DEPARTAMENTAL
 
188

Length

Max length39
Median length2
Mean length12.712076
Min length2

Characters and Unicode

Total characters6614182
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGENERACION E - GRATUIDAD
2nd rowGENERACION E - GRATUIDAD
3rd rowGENERACION E - GRATUIDAD
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 269228
51.7%
GENERACION E - GRATUIDAD 246190
47.3%
GENERACION E - EXCELENCIA NACIONAL 4701
 
0.9%
GENERACION E - EXCELENCIA DEPARTAMENTAL 188
 
< 0.1%

Length

2024-10-24T11:45:09.256280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:09.300239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 269228
21.1%
generacion 251079
19.6%
e 251079
19.6%
251079
19.6%
gratuidad 246190
19.3%
excelencia 4889
 
0.4%
nacional 4701
 
0.4%
departamental 188
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 785865
11.9%
E 768280
11.6%
A 758314
11.5%
758126
11.5%
O 525008
7.9%
I 506859
7.7%
R 497457
7.5%
G 497269
7.5%
D 492568
7.4%
C 265558
 
4.0%
Other values (7) 758878
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6614182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 785865
11.9%
E 768280
11.6%
A 758314
11.5%
758126
11.5%
O 525008
7.9%
I 506859
7.7%
R 497457
7.5%
G 497269
7.5%
D 492568
7.4%
C 265558
 
4.0%
Other values (7) 758878
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6614182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 785865
11.9%
E 768280
11.6%
A 758314
11.5%
758126
11.5%
O 525008
7.9%
I 506859
7.7%
R 497457
7.5%
G 497269
7.5%
D 492568
7.4%
C 265558
 
4.0%
Other values (7) 758878
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6614182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 785865
11.9%
E 768280
11.6%
A 758314
11.5%
758126
11.5%
O 525008
7.9%
I 506859
7.7%
R 497457
7.5%
G 497269
7.5%
D 492568
7.4%
C 265558
 
4.0%
Other values (7) 758878
11.5%

ESTU_INSE_INDIVIDUAL
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct455002
Distinct (%)92.7%
Missing29380
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean50.809918
Minimum14.281577
Maximum84.836366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-10-24T11:45:09.357299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14.281577
5-th percentile35.722404
Q144.314347
median50.633782
Q356.832224
95-th percentile67.069611
Maximum84.836366
Range70.554789
Interquartile range (IQR)12.517877

Descriptive statistics

Standard deviation9.4448126
Coefficient of variation (CV)0.18588522
Kurtosis0.049240319
Mean50.809918
Median Absolute Deviation (MAD)6.2578823
Skewness0.19079955
Sum24943960
Variance89.204484
MonotonicityNot monotonic
2024-10-24T11:45:09.413068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.93206692 219
 
< 0.1%
74.62706922 175
 
< 0.1%
77.56604092 161
 
< 0.1%
84.83636609 153
 
< 0.1%
82.39573843 151
 
< 0.1%
80.55237448 124
 
< 0.1%
82.8069781 116
 
< 0.1%
74.73906407 94
 
< 0.1%
73.4443508 87
 
< 0.1%
73.1884579 87
 
< 0.1%
Other values (454992) 489560
94.1%
(Missing) 29380
 
5.6%
ValueCountFrequency (%)
14.28157733 1
< 0.1%
14.34691534 1
< 0.1%
14.4916569 1
< 0.1%
14.96183456 1
< 0.1%
15.31796704 2
< 0.1%
16.10287682 1
< 0.1%
16.16610347 1
< 0.1%
16.18742044 1
< 0.1%
16.51733647 2
< 0.1%
16.57954254 1
< 0.1%
ValueCountFrequency (%)
84.83636609 153
< 0.1%
84.83199363 2
 
< 0.1%
84.71808729 1
 
< 0.1%
84.59633497 1
 
< 0.1%
84.03430703 2
 
< 0.1%
83.93844879 1
 
< 0.1%
83.59551988 8
 
< 0.1%
83.16748951 7
 
< 0.1%
83.05939172 2
 
< 0.1%
83.02546494 1
 
< 0.1%

ESTU_NSE_INDIVIDUAL
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing29380
Missing (%)5.6%
Memory size25.9 MiB
3.0
192877 
2.0
181586 
1.0
75349 
4.0
41115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1472781
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 192877
37.1%
2.0 181586
34.9%
1.0 75349
 
14.5%
4.0 41115
 
7.9%
(Missing) 29380
 
5.6%

Length

2024-10-24T11:45:09.465190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:09.504241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 192877
39.3%
2.0 181586
37.0%
1.0 75349
 
15.3%
4.0 41115
 
8.4%

Most occurring characters

ValueCountFrequency (%)
. 490927
33.3%
0 490927
33.3%
3 192877
 
13.1%
2 181586
 
12.3%
1 75349
 
5.1%
4 41115
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1472781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 490927
33.3%
0 490927
33.3%
3 192877
 
13.1%
2 181586
 
12.3%
1 75349
 
5.1%
4 41115
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1472781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 490927
33.3%
0 490927
33.3%
3 192877
 
13.1%
2 181586
 
12.3%
1 75349
 
5.1%
4 41115
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1472781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 490927
33.3%
0 490927
33.3%
3 192877
 
13.1%
2 181586
 
12.3%
1 75349
 
5.1%
4 41115
 
2.8%

ESTU_NSE_ESTABLECIMIENTO
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing15484
Missing (%)3.0%
Memory size25.9 MiB
2.0
261346 
3.0
194861 
1.0
 
25208
4.0
 
23408

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1514469
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 261346
50.2%
3.0 194861
37.5%
1.0 25208
 
4.8%
4.0 23408
 
4.5%
(Missing) 15484
 
3.0%

Length

2024-10-24T11:45:09.550225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T11:45:09.589604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 261346
51.8%
3.0 194861
38.6%
1.0 25208
 
5.0%
4.0 23408
 
4.6%

Most occurring characters

ValueCountFrequency (%)
. 504823
33.3%
0 504823
33.3%
2 261346
17.3%
3 194861
 
12.9%
1 25208
 
1.7%
4 23408
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1514469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 504823
33.3%
0 504823
33.3%
2 261346
17.3%
3 194861
 
12.9%
1 25208
 
1.7%
4 23408
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1514469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 504823
33.3%
0 504823
33.3%
2 261346
17.3%
3 194861
 
12.9%
1 25208
 
1.7%
4 23408
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1514469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 504823
33.3%
0 504823
33.3%
2 261346
17.3%
3 194861
 
12.9%
1 25208
 
1.7%
4 23408
 
1.5%

Interactions

2024-10-24T11:44:46.515190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:12.463423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:13.989492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.453344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:17.009335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.154193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:20.984473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.500138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:23.951434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.508679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:26.911538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.369279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:30.026418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.489572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:33.354725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.225902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:37.081491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.607114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.250837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.806385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.484689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:45.041123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.584107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:12.556801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:14.049992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.523689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:17.076694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.235241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:21.054814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.564628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:24.015353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.575954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:26.972368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.439713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:30.093291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.550580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:33.435111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.304812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:37.156218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.683549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.321898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.879982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.553710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:45.114273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.651692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:12.623583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:14.110771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.590289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:17.139274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.306513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:21.121255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.624549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:24.081456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.639024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:27.039778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.507064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:30.158405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.612925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:33.504646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.394983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:37.227673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.750212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.390713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.945893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.624112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:45.178664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.723329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:12.710021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:14.177448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.652103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:17.214283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.380718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:21.205961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.714821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:24.159398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.710413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:27.108545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.575888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:30.231838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.679340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:33.574231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.526140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:37.306344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.821237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.464493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:42.019147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.702953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:45.244735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.800460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:12.816954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:14.240078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.737846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:17.285097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.457986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:21.280111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.791596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:24.234203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.777001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:27.170927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.648176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:30.300184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.753292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:33.658922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.642271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:37.384934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.893003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.535579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:42.090714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.771650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:45.312939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.868599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:12.887597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:14.307910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.806481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:17.355419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.546450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:21.345320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.861229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:24.297066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.850556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:27.236625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.729633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:30.372249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.825038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:33.735612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.771213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:37.453081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.959916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.620136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:42.159737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.840928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:45.376782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.932616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:12.956384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:14.368123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.874504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:17.421027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.614404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:21.442812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.926873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:24.357235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.915683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:27.296691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.797435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:30.439966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.890567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:33.798785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.861965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:37.520027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:39.024990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.687054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:42.235048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.916536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:45.438765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.996184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:13.030921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:14.427298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.950046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:17.490104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.687563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:21.511034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.990016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-10-24T11:44:26.536154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-10-24T11:44:29.635649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.098856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:32.573772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:34.732463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:36.631998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.163546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:39.811149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.398909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.065286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:44.575792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.105438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:47.691507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:13.669032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.103465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:16.657413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:18.770303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:20.634144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.164961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:23.613747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.162939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:26.602395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.018773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:29.701923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.167621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:32.640200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:34.806787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:36.712883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.235155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:39.883558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.465522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.135815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:44.637135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.173308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:47.755271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:13.733147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.177004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:16.730225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:18.856897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:20.698248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.229140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:23.686700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.226101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:26.662424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.082026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:29.770185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.234760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:32.704637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:34.898974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:36.780123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.304696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:39.953589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.530716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.201812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:44.790779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.244479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:47.819622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:13.792659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.255330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:16.798284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:18.935718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:20.769350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.301196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:23.753720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.289181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:26.721486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.146905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:29.829278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.298535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:32.785867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:34.978223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:36.853388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.373182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.020499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.602294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.271487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:44.848020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.312661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:47.883639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:13.863808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.316301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:16.868942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.005995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:20.843359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.372576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:23.819633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.370637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:26.789435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.227577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:29.893633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.364304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:32.853447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.055224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:36.928569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.442551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.105381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.670031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.345504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:44.909725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.379970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:47.943833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:13.926151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:15.378038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:16.938932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:19.079663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:20.913035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:22.435859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:23.885241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:25.437270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:26.849636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:28.297406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:29.956417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:31.426213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:33.261453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:35.135592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:37.003960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:38.512130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:40.181501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:41.738435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:43.410266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:44.972214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-24T11:44:46.445692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-24T11:45:09.664210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
COLE_AREA_UBICACIONCOLE_BILINGUECOLE_CALENDARIOCOLE_CARACTERCOLE_CODIGO_ICFESCOLE_COD_DANE_ESTABLECIMIENTOCOLE_COD_DANE_SEDECOLE_COD_DEPTO_UBICACIONCOLE_COD_MCPIO_UBICACIONCOLE_DEPTO_UBICACIONCOLE_GENEROCOLE_JORNADACOLE_NATURALEZACOLE_SEDE_PRINCIPALDESEMP_C_NATURALESDESEMP_INGLESDESEMP_LECTURA_CRITICADESEMP_MATEMATICASDESEMP_SOCIALES_CIUDADANASESTU_COD_DEPTO_PRESENTACIONESTU_COD_MCPIO_PRESENTACIONESTU_COD_RESIDE_DEPTOESTU_COD_RESIDE_MCPIOESTU_DEDICACIONINTERNETESTU_DEDICACIONLECTURADIARIAESTU_DEPTO_PRESENTACIONESTU_DEPTO_RESIDEESTU_ESTADOINVESTIGACIONESTU_GENERACION-EESTU_GENEROESTU_HORASSEMANATRABAJAESTU_INSE_INDIVIDUALESTU_NACIONALIDADESTU_NSE_ESTABLECIMIENTOESTU_NSE_INDIVIDUALESTU_PAIS_RESIDEESTU_PRIVADO_LIBERTADESTU_TIENEETNIAESTU_TIPODOCUMENTOESTU_TIPOREMUNERACIONFAMI_COMECARNEPESCADOHUEVOFAMI_COMECEREALFRUTOSLEGUMBREFAMI_COMELECHEDERIVADOSFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_NUMLIBROSFAMI_PERSONASHOGARFAMI_SITUACIONECONOMICAFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENECONSOLAVIDEOJUEGOSFAMI_TIENEHORNOMICROOGASFAMI_TIENEINTERNETFAMI_TIENELAVADORAFAMI_TIENEMOTOCICLETAFAMI_TIENESERVICIOTVFAMI_TRABAJOLABORMADREFAMI_TRABAJOLABORPADREPERCENTIL_C_NATURALESPERCENTIL_GLOBALPERCENTIL_INGLESPERCENTIL_LECTURA_CRITICAPERCENTIL_MATEMATICASPERCENTIL_SOCIALES_CIUDADANASPERIODOPUNT_C_NATURALESPUNT_GLOBALPUNT_INGLESPUNT_LECTURA_CRITICAPUNT_MATEMATICASPUNT_SOCIALES_CIUDADANAS
COLE_AREA_UBICACION1.0000.0450.0230.1470.3000.6550.6690.1920.1880.2680.0710.1020.1720.0910.1380.1540.1700.1560.1590.1850.1810.0020.1790.1830.0440.2540.2570.0010.1790.0050.1330.3290.0130.4840.3160.0130.0030.1870.0220.0990.1060.0690.1270.0330.2160.2120.2190.0890.0770.0720.0730.2260.0950.1240.3000.1640.1070.1310.2110.3140.1470.1910.1700.1820.1670.1670.0180.1440.1860.1670.1790.1640.164
COLE_BILINGUE0.0451.0000.3080.0440.0630.0950.0970.0580.0570.1390.0040.1330.0990.0040.0960.2440.0800.0900.0820.0570.0550.0000.0580.0200.0080.1410.1410.0270.0460.0040.0150.0390.0320.0680.0330.0320.0020.0850.0180.0210.0360.0490.0420.0260.1350.1480.2350.0870.0110.0030.0810.0280.0680.0420.0120.0000.0180.0150.0920.1160.0290.0390.0570.0270.0340.0300.2800.1070.1280.2520.0990.1010.101
COLE_CALENDARIO0.0230.3081.0000.0720.0920.2480.2470.1580.1580.2050.0550.1520.2960.0370.1230.2670.1140.1080.1020.1580.1580.0000.1560.0520.0200.2050.2020.1030.0920.0140.0400.0100.0310.0130.0100.0310.0020.0360.0500.0280.0720.0700.0730.0360.1650.1760.2590.1030.0430.0080.1760.1080.1450.1210.0940.0660.0480.0670.1460.1930.0360.0390.0400.0330.0370.0300.8640.1350.1520.2700.1330.1200.124
COLE_CARACTER0.1470.0440.0721.0000.2150.1930.1910.1590.1580.2740.0350.1820.3290.0590.0500.0800.0490.0460.0520.1590.1570.0070.1570.0390.0090.2720.2740.0100.0630.0280.0250.1030.0060.1590.0960.0060.0050.0790.0140.0180.0390.0290.0570.0140.0760.0760.0980.0580.0160.0190.1080.0850.1130.0970.0960.0880.0890.0660.0660.0770.0460.0570.0670.0480.0460.0490.1010.0530.0640.0820.0530.0510.055
COLE_CODIGO_ICFES0.3000.0630.0920.2151.000-0.108-0.1080.0220.0190.1650.1010.3800.2480.2230.0970.0930.0910.0940.0940.0200.0170.0220.0190.0470.0170.1590.1640.0120.0680.0180.040-0.1520.0000.1640.1120.0000.0140.1290.0310.0350.0420.0370.0530.0190.0610.0580.0730.0670.0350.0240.1220.1320.0920.0960.1420.0780.0550.0800.0480.056-0.125-0.137-0.132-0.119-0.124-0.1210.104-0.135-0.149-0.147-0.129-0.134-0.130
COLE_COD_DANE_ESTABLECIMIENTO0.6550.0950.2480.193-0.1081.0000.9900.3880.3870.2620.0800.2250.8350.0940.1270.1580.1260.1190.1290.3800.3790.3820.3800.0910.0260.2540.2550.0410.1380.0420.0630.1200.0130.3260.2380.0130.0000.1700.0160.0520.1030.0710.1170.0340.1270.1210.1480.1120.0540.0360.2660.2580.2170.2140.2920.1700.1290.1530.1070.1320.0850.0800.0970.0600.0710.0690.3080.1120.1090.1400.0860.0960.091
COLE_COD_DANE_SEDE0.6690.0970.2470.191-0.1080.9901.0000.3870.3860.2640.0800.2260.8330.1000.1270.1570.1260.1190.1290.3790.3780.3800.3790.0920.0260.2550.2570.0410.1380.0420.0640.1160.0130.3260.2390.0130.0000.1700.0160.0530.1030.0710.1170.0340.1270.1220.1480.1120.0540.0370.2650.2590.2170.2140.2950.1720.1320.1530.1080.1330.0830.0770.0950.0580.0690.0670.3070.1100.1060.1380.0840.0940.089
COLE_COD_DEPTO_UBICACION0.1920.0580.1580.1590.0220.3880.3871.0000.9981.0000.0720.1370.1840.0920.0520.0590.0590.0550.0570.9820.9800.9910.9890.0790.0300.9730.9870.0290.1530.0120.055-0.1400.0140.1990.1370.0140.0130.2110.0190.0500.0840.0500.1150.0300.0470.0470.1100.0640.0430.0320.0740.1820.1610.1790.2190.1920.2220.1380.0570.068-0.002-0.029-0.046-0.037-0.018-0.0390.1810.007-0.018-0.027-0.027-0.008-0.032
COLE_COD_MCPIO_UBICACION0.1880.0570.1580.1580.0190.3870.3860.9981.0000.9850.0690.1290.1820.0890.0480.0570.0560.0510.0540.9810.9820.9890.9910.0780.0300.9590.9730.0290.1550.0120.055-0.1480.0130.1980.1380.0130.0130.1910.0190.0510.0820.0510.1150.0300.0480.0480.1090.0650.0430.0320.0740.1820.1610.1800.2180.1920.2220.1370.0570.068-0.006-0.034-0.052-0.042-0.022-0.0440.1810.003-0.024-0.033-0.033-0.013-0.037
COLE_DEPTO_UBICACION0.2680.1390.2050.2740.1650.2620.2641.0000.9851.0000.1120.2310.2680.1180.1350.1100.1270.1360.1290.9730.9590.0290.9730.1040.0540.9760.9910.0370.2340.0260.0770.1200.0100.3030.1970.0100.0250.4630.0360.0860.1270.1090.1520.0620.0660.0680.1660.1200.0940.0580.1760.3020.2340.2290.3270.2580.2640.1830.0760.0810.0830.0930.0800.0780.0840.0780.2380.0860.0900.0830.0820.0830.078
COLE_GENERO0.0710.0040.0550.0350.1010.0800.0800.0720.0690.1121.0000.1120.1200.0370.0910.1150.0870.0840.0930.0700.0670.0000.0680.0490.0330.1090.1090.0050.0620.1470.0510.1250.0080.1630.1160.0080.0030.0240.0340.0480.0530.0360.0600.0200.1070.1040.1040.0680.0280.0140.0990.0970.0760.0800.0860.0580.0520.0550.0780.0810.0890.1020.1030.0880.0870.0920.0610.0960.1090.1150.0920.0940.097
COLE_JORNADA0.1020.1330.1520.1820.3800.2250.2260.1370.1290.2310.1121.0000.5390.0580.1780.1810.1580.1720.1650.1350.1290.0080.1300.0690.0340.2290.2310.0170.1180.0200.0910.1710.0160.2700.2040.0160.0200.1070.1640.0700.0960.0710.1100.0450.1450.1370.1420.1300.0510.0220.2560.1950.1960.1830.1400.1030.0980.1160.1060.1110.1410.1570.1510.1260.1410.1280.1760.1450.1610.1690.1300.1450.133
COLE_NATURALEZA0.1720.0990.2960.3290.2480.8350.8330.1840.1820.2680.1200.5391.0000.1260.2430.3620.2260.2170.2420.1850.1810.0020.1810.1680.0520.2700.2690.0300.2800.0500.1120.4550.0300.5370.4290.0300.0030.1140.0300.0870.1930.1430.2270.0820.3690.3520.3870.2340.1160.0300.3170.2750.2630.2560.2610.1710.1130.1590.2890.3180.2200.2500.2920.2070.2090.2220.2990.2560.2890.3620.2390.2420.252
COLE_SEDE_PRINCIPAL0.0910.0040.0370.0590.2230.0940.1000.0920.0890.1180.0370.0580.1261.0000.0530.0650.0540.0540.0540.0930.0890.0000.0910.0390.0130.1190.1190.0050.0450.0010.0330.0820.0090.0980.0770.0090.0410.0050.0160.0280.0280.0190.0460.0120.0740.0690.0580.0390.0170.0130.0470.0540.0380.0400.0500.0420.0160.0330.0570.0610.0530.0610.0600.0530.0560.0520.0380.0560.0640.0670.0560.0590.055
DESEMP_C_NATURALES0.1380.0960.1230.0500.0970.1270.1270.0520.0480.1350.0910.1780.2430.0531.0000.3950.4060.4410.4790.0530.0480.0000.0490.1250.0760.1360.1370.0210.2530.0840.1080.2270.0130.2300.2120.0130.0120.1420.1150.0750.1100.0860.1320.0570.2060.1990.1570.1610.0760.0640.1990.2610.1460.1550.2480.1410.0890.1130.1300.1450.7700.6110.3900.4390.4670.4780.1450.9260.6560.4140.4470.4960.498
DESEMP_INGLES0.1540.2440.2670.0800.0930.1580.1570.0590.0570.1100.1150.1810.3620.0650.3951.0000.3480.3420.3820.0630.0610.0000.0600.1170.0610.1140.1130.0380.2040.0550.1030.2540.0240.3040.2720.0240.0120.1160.0840.0890.1250.1000.1480.0500.2270.2270.2250.1880.0690.0420.2790.3050.2320.2320.2840.1720.1240.1490.1500.1720.3330.3860.6720.3020.3020.3200.3360.3640.4290.7720.3250.3270.348
DESEMP_LECTURA_CRITICA0.1700.0800.1140.0490.0910.1260.1260.0590.0560.1270.0870.1580.2260.0540.4060.3481.0000.4010.4570.0600.0570.0000.0570.1300.0860.1270.1280.0210.1940.0240.1110.2160.0120.2210.2040.0120.0140.1530.1060.0730.1000.0840.1260.0530.1950.1860.1550.1530.0770.0730.1760.2620.1310.1490.2600.1460.0940.1160.1270.1400.4350.6110.3480.8510.4270.4890.1360.4380.5870.3610.7890.4320.491
DESEMP_MATEMATICAS0.1560.0900.1080.0460.0940.1190.1190.0550.0510.1360.0840.1720.2170.0540.4410.3420.4011.0000.4100.0550.0510.0000.0520.1250.0580.1350.1370.0180.2300.1190.0910.2170.0150.2200.2040.0150.0110.1460.1210.0570.1090.0820.1290.0570.1960.1860.1490.1490.0730.0620.1900.2590.1450.1460.2520.1440.0800.1130.1230.1370.4560.5980.3420.4230.8460.4210.1250.4730.5990.3570.4260.9130.432
DESEMP_SOCIALES_CIUDADANAS0.1590.0820.1020.0520.0940.1290.1290.0570.0540.1290.0930.1650.2420.0540.4790.3820.4570.4101.0000.0580.0560.0000.0560.1280.0940.1300.1310.0170.2370.0460.1150.2310.0110.2370.2160.0110.0100.1400.1000.0740.1010.0900.1300.0530.2040.1970.1590.1640.0780.0690.1870.2670.1410.1520.2590.1380.1080.1090.1320.1440.4910.6420.3780.5050.4400.8360.1180.5000.6420.3940.5000.4500.933
ESTU_COD_DEPTO_PRESENTACION0.1850.0570.1580.1590.0200.3800.3790.9820.9810.9730.0700.1350.1850.0930.0530.0630.0600.0550.0581.0000.9980.9880.9870.0800.0301.0000.9820.0290.1530.0120.056-0.1420.0140.2000.1390.0140.0130.2080.0190.0520.0850.0510.1160.0290.0480.0480.1110.0640.0420.0320.0710.1820.1640.1810.2210.1930.2240.1380.0570.068-0.003-0.031-0.049-0.038-0.019-0.0410.1810.005-0.021-0.030-0.030-0.011-0.034
ESTU_COD_MCPIO_PRESENTACION0.1810.0550.1580.1570.0170.3790.3780.9800.9820.9590.0670.1290.1810.0890.0480.0610.0570.0510.0560.9981.0000.9870.9880.0790.0300.9860.9670.0290.1550.0120.056-0.1490.0130.1990.1390.0130.0130.1880.0190.0520.0830.0510.1160.0290.0490.0490.1100.0650.0420.0320.0710.1820.1640.1820.2200.1930.2240.1370.0570.068-0.007-0.035-0.054-0.043-0.023-0.0460.1810.001-0.026-0.036-0.035-0.015-0.039
ESTU_COD_RESIDE_DEPTO0.0020.0000.0000.0070.0220.3820.3800.9910.9890.0290.0000.0080.0020.0000.0000.0000.0000.0000.0000.9880.9871.0000.9980.0000.0000.0291.0000.0000.0050.0000.000-0.1430.0000.0040.0000.0000.0000.0000.0050.0000.0020.0000.0000.0000.0000.0040.0020.0020.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.000-0.004-0.031-0.049-0.039-0.020-0.0420.0000.004-0.021-0.031-0.030-0.011-0.035
ESTU_COD_RESIDE_MCPIO0.1790.0580.1560.1570.0190.3800.3790.9890.9910.9730.0680.1300.1810.0910.0490.0600.0570.0520.0560.9870.9880.9981.0000.0790.0300.9680.9870.0290.1550.0120.055-0.1510.0130.1980.1380.0130.0130.1910.0180.0520.0830.0500.1160.0290.0480.0490.1100.0650.0430.0320.0700.1820.1630.1810.2190.1920.2230.1380.0570.068-0.008-0.036-0.055-0.044-0.024-0.0460.179-0.000-0.027-0.037-0.036-0.016-0.040
ESTU_DEDICACIONINTERNET0.1830.0200.0520.0390.0470.0910.0920.0790.0780.1040.0490.0690.1680.0390.1250.1170.1300.1250.1280.0800.0790.0000.0791.0000.0700.1040.1040.0040.1160.0350.0780.2370.0040.1790.2590.0041.0000.1050.0530.0600.1790.1030.1820.0560.1400.1310.1350.1260.0470.0360.1590.3230.1830.2350.4270.2520.0350.2350.1020.1270.1140.1330.1230.1190.1150.1150.0640.1130.1310.1240.1180.1150.115
ESTU_DEDICACIONLECTURADIARIA0.0440.0080.0200.0090.0170.0260.0260.0300.0300.0540.0330.0340.0520.0130.0760.0610.0860.0580.0940.0300.0300.0000.0300.0701.0000.0540.0550.0030.0410.1340.0310.0460.0080.0440.0500.0081.0000.0360.0260.0350.0410.0400.0400.0110.0510.0500.0330.1170.0140.0360.0360.0550.0660.0550.0620.0470.0340.0380.0340.0380.0700.0810.0630.0810.0560.0860.0240.0690.0790.0620.0780.0550.084
ESTU_DEPTO_PRESENTACION0.2540.1410.2050.2720.1590.2540.2550.9730.9590.9760.1090.2290.2700.1190.1360.1140.1270.1350.1301.0000.9860.0290.9680.1040.0541.0000.9820.0370.2350.0260.0780.1210.0100.3030.1990.0100.0250.4580.0360.0870.1280.1100.1530.0620.0670.0690.1670.1220.0930.0580.1780.3030.2390.2320.3280.2590.2660.1830.0760.0820.0830.0930.0810.0780.0840.0790.2390.0870.0910.0860.0820.0830.078
ESTU_DEPTO_RESIDE0.2570.1410.2020.2740.1640.2550.2570.9870.9730.9910.1090.2310.2690.1190.1370.1130.1280.1370.1310.9820.9671.0000.9870.1040.0550.9821.0000.0370.2350.0260.0780.1210.0100.3040.1990.0100.0250.4630.0360.0860.1280.1100.1530.0620.0670.0690.1670.1220.0940.0580.1780.3040.2380.2320.3290.2590.2660.1840.0760.0820.0840.0940.0810.0790.0850.0790.2360.0870.0910.0860.0830.0840.079
ESTU_ESTADOINVESTIGACION0.0010.0270.1030.0100.0120.0410.0410.0290.0290.0370.0050.0170.0300.0050.0210.0380.0210.0180.0170.0290.0290.0000.0290.0040.0030.0370.0371.0000.0110.0010.0000.0070.0140.0040.0050.0140.0000.0020.0020.0040.0060.0120.0110.0050.0230.0200.0340.0160.0040.0030.0170.0080.0100.0110.0070.0030.0030.0060.0300.0260.0000.0030.0040.0050.0020.0040.1050.0220.0280.0380.0320.0210.024
ESTU_GENERACION-E0.1790.0460.0920.0630.0680.1380.1380.1530.1550.2340.0620.1180.2800.0450.2530.2040.1940.2300.2370.1530.1550.0050.1550.1160.0410.2350.2350.0111.0000.0410.0650.2290.0320.2300.2210.0320.0010.0720.0480.0450.1070.0860.1270.0490.1630.1540.2060.1180.0830.0250.2360.3150.2200.2460.3140.1870.0910.1690.1320.1540.2010.2180.1970.1980.1990.2040.1230.2720.3910.2070.2310.2580.252
ESTU_GENERO0.0050.0040.0140.0280.0180.0420.0420.0120.0120.0260.1470.0200.0500.0010.0840.0550.0240.1190.0460.0120.0120.0000.0120.0350.1340.0260.0260.0010.0411.0000.1760.0650.0020.0240.0620.0020.0150.0080.0430.1800.0500.0290.0550.0500.0780.0660.0730.0250.0190.0480.0500.0370.1740.0880.0380.0460.0390.0060.0600.0530.0890.0750.0510.0240.1290.0510.0190.0870.0730.0550.0230.1280.052
ESTU_HORASSEMANATRABAJA0.1330.0150.0400.0250.0400.0630.0640.0550.0550.0770.0510.0910.1120.0330.1080.1030.1110.0910.1150.0560.0560.0000.0550.0780.0310.0780.0780.0000.0650.1761.0000.1340.0060.1240.1480.0061.0000.0630.0900.3790.0650.0350.0750.0260.0960.0960.0550.0520.0350.0430.0670.1610.0640.0980.1730.1150.0820.1070.0790.0930.0980.1110.1070.1030.0850.1030.0390.0970.1090.1070.1020.0850.103
ESTU_INSE_INDIVIDUAL0.3290.0390.0100.103-0.1520.1200.116-0.140-0.1480.1200.1250.1710.4550.0820.2270.2540.2160.2170.231-0.142-0.149-0.143-0.1510.2370.0460.1210.1210.0070.2290.0650.1341.0000.0110.4470.8910.0111.0000.1590.0580.1110.3060.2180.3410.1020.3160.3050.2500.2850.0810.0650.5270.6900.4740.5250.7070.5130.0840.4580.2290.2510.3760.4330.4340.3830.3820.3771.0000.3760.4330.4330.3830.3820.377
ESTU_NACIONALIDAD0.0130.0320.0310.0060.0000.0130.0130.0140.0130.0100.0080.0160.0300.0090.0130.0240.0120.0150.0110.0140.0130.0000.0130.0040.0080.0100.0100.0140.0320.0020.0060.0111.0000.0210.0191.0000.0000.0160.2630.0050.0030.0080.0080.0110.0160.0110.0260.0220.0090.0070.0350.0350.0220.0160.0150.0410.0390.0170.0140.0160.0050.0050.0080.0060.0060.0050.0340.0060.0090.0190.0090.0070.001
ESTU_NSE_ESTABLECIMIENTO0.4840.0680.0130.1590.1640.3260.3260.1990.1980.3030.1630.2700.5370.0980.2300.3040.2210.2200.2370.2000.1990.0040.1980.1790.0440.3030.3040.0040.2300.0240.1240.4470.0211.0000.4160.0210.0090.2110.0680.0900.1590.1130.1880.0690.2990.2920.3480.1850.0900.0410.3520.4410.3110.3290.4620.3070.1630.2540.2340.2780.2440.2840.3010.2390.2440.2461.0000.2430.2800.3090.2330.2400.246
ESTU_NSE_INDIVIDUAL0.3160.0330.0100.0960.1120.2380.2390.1370.1380.1970.1160.2040.4290.0770.2120.2720.2040.2040.2160.1390.1390.0000.1380.2590.0500.1990.1990.0050.2210.0620.1480.8910.0190.4161.0000.0191.0000.1510.0960.1070.2810.2040.3160.1260.4690.4480.3300.2650.0890.0590.4930.6680.4460.5040.6750.4920.0740.4390.3620.4060.2280.2610.2770.2210.2260.2251.0000.2240.2560.2790.2150.2220.224
ESTU_PAIS_RESIDE0.0130.0320.0310.0060.0000.0130.0130.0140.0130.0100.0080.0160.0300.0090.0130.0240.0120.0150.0110.0140.0130.0000.0130.0040.0080.0100.0100.0140.0320.0020.0060.0111.0000.0210.0191.0000.0000.0160.2630.0050.0030.0080.0080.0110.0160.0110.0260.0220.0090.0070.0350.0350.0220.0160.0150.0410.0390.0170.0140.0160.0050.0050.0080.0060.0060.0050.0340.0060.0090.0190.0090.0070.001
ESTU_PRIVADO_LIBERTAD0.0030.0020.0020.0050.0140.0000.0000.0130.0130.0250.0030.0200.0030.0410.0120.0120.0140.0110.0100.0130.0130.0000.0131.0001.0000.0250.0250.0000.0010.0151.0001.0000.0000.0091.0000.0001.0000.0030.0121.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0130.0150.0140.0160.0120.0110.0000.0120.0140.0130.0130.0110.011
ESTU_TIENEETNIA0.1870.0850.0360.0790.1290.1700.1700.2110.1910.4630.0240.1070.1140.0050.1420.1160.1530.1460.1400.2080.1880.0000.1910.1050.0360.4580.4630.0020.0720.0080.0630.1590.0160.2110.1510.0160.0031.0000.0260.0380.1050.0630.1140.0520.0960.0740.1880.0600.0950.0180.0680.1210.0700.0840.1660.1270.0270.0690.0800.1070.1490.1820.1350.1640.1550.1480.0370.1470.1750.1280.1630.1540.145
ESTU_TIPODOCUMENTO0.0220.0180.0500.0140.0310.0160.0160.0190.0190.0360.0340.1640.0300.0160.1150.0840.1060.1210.1000.0190.0190.0050.0180.0530.0260.0360.0360.0020.0480.0430.0900.0580.2630.0680.0960.2630.0120.0261.0000.0810.0520.0240.0480.0280.0570.0480.0310.0560.0360.0130.0800.1140.0410.0660.0930.0950.0450.0790.0290.0300.0720.0780.0640.0660.0740.0610.0270.0730.0750.0660.0680.0730.059
ESTU_TIPOREMUNERACION0.0990.0210.0280.0180.0350.0520.0530.0500.0510.0860.0480.0700.0870.0280.0750.0890.0730.0570.0740.0520.0520.0000.0520.0600.0350.0870.0860.0040.0450.1800.3790.1110.0050.0900.1070.0051.0000.0380.0811.0000.0440.0150.0490.0320.0910.0970.0500.0370.0370.0560.0490.1020.0360.0510.1090.0750.0820.0710.0890.1000.0790.0860.0930.0770.0620.0770.0230.0780.0860.0920.0770.0640.077
FAMI_COMECARNEPESCADOHUEVO0.1060.0360.0720.0390.0420.1030.1030.0840.0820.1270.0530.0960.1930.0280.1100.1250.1000.1090.1010.0850.0830.0020.0830.1790.0410.1280.1280.0060.1070.0500.0650.3060.0030.1590.2810.0031.0000.1050.0520.0441.0000.1970.2840.0760.1530.1390.1570.1200.0540.0870.2220.2790.2130.2690.2770.2440.0370.2370.1120.1310.1120.1240.1200.1030.1140.1000.0940.1150.1270.1290.1060.1170.105
FAMI_COMECEREALFRUTOSLEGUMBRE0.0690.0490.0700.0290.0370.0710.0710.0500.0510.1090.0360.0710.1430.0190.0860.1000.0840.0820.0900.0510.0510.0000.0500.1030.0400.1100.1100.0120.0860.0290.0350.2180.0080.1130.2040.0081.0000.0630.0240.0150.1971.0000.2260.0640.1080.1030.1200.1230.0370.0710.1720.2050.1650.1930.1880.1320.0190.1410.0840.0950.0860.0980.0910.0860.0850.0890.0980.0900.1020.1020.0890.0890.093
FAMI_COMELECHEDERIVADOS0.1270.0420.0730.0570.0530.1170.1170.1150.1150.1520.0600.1100.2270.0460.1320.1480.1260.1290.1300.1160.1160.0000.1160.1820.0400.1530.1530.0110.1270.0550.0750.3410.0080.1880.3160.0081.0000.1140.0480.0490.2840.2261.0000.0780.1720.1630.1780.1520.0590.0830.2400.3150.2390.2890.3000.2550.0410.2350.1260.1410.1350.1540.1440.1310.1370.1320.0980.1380.1550.1530.1330.1390.135
FAMI_CUARTOSHOGAR0.0330.0260.0360.0140.0190.0340.0340.0300.0300.0620.0200.0450.0820.0120.0570.0500.0530.0570.0530.0290.0290.0000.0290.0560.0110.0620.0620.0050.0490.0500.0260.1020.0110.0690.1260.0111.0000.0520.0280.0320.0760.0640.0781.0000.0530.0520.0710.0800.2630.0530.1580.1630.1380.1510.1600.1460.0780.1370.0430.0510.0470.0510.0450.0420.0460.0420.0440.0470.0500.0470.0430.0460.042
FAMI_EDUCACIONMADRE0.2160.1350.1650.0760.0610.1270.1270.0470.0480.0660.1070.1450.3690.0740.2060.2270.1950.1960.2040.0480.0490.0000.0480.1400.0510.0670.0670.0230.1630.0780.0960.3160.0160.2990.4690.0161.0000.0960.0570.0910.1530.1080.1720.0531.0000.3030.1820.2000.0750.0360.3290.3670.2610.2890.3530.2590.0770.2200.2230.1480.1170.1330.1340.1130.1160.1150.2140.1330.1420.1640.1260.1230.123
FAMI_EDUCACIONPADRE0.2120.1480.1760.0760.0580.1210.1220.0470.0480.0680.1040.1370.3520.0690.1990.2270.1860.1860.1970.0480.0490.0040.0490.1310.0500.0690.0690.0200.1540.0660.0960.3050.0110.2920.4480.0111.0000.0740.0480.0970.1390.1030.1630.0520.3031.0000.1830.1900.0680.0370.3170.3330.2620.2750.3270.2340.1040.2060.1310.2490.1110.1250.1300.1060.1080.1080.2270.1290.1370.1640.1210.1170.118
FAMI_ESTRATOVIVIENDA0.2190.2350.2590.0980.0730.1480.1480.1100.1090.1660.1040.1420.3870.0580.1570.2250.1550.1490.1590.1110.1100.0020.1100.1350.0330.1670.1670.0340.2060.0730.0550.2500.0260.3480.3300.0261.0000.1880.0310.0500.1570.1200.1780.0710.1820.1831.0000.1700.0670.0510.3660.3760.3330.3370.3850.2590.0950.2460.1440.1680.1030.1240.1350.1060.1030.1050.3360.1180.1410.1880.1180.1160.119
FAMI_NUMLIBROS0.0890.0870.1030.0580.0670.1120.1120.0640.0650.1200.0680.1300.2340.0390.1610.1880.1530.1490.1640.0640.0650.0020.0650.1260.1170.1220.1220.0160.1180.0250.0520.2850.0220.1850.2650.0221.0000.0600.0560.0370.1200.1230.1520.0800.2000.1900.1701.0000.0310.0270.2330.2900.1940.2220.2370.1640.0590.1390.1440.1510.1620.1810.1730.1560.1570.1630.1370.1690.1880.1910.1600.1620.170
FAMI_PERSONASHOGAR0.0770.0110.0430.0160.0350.0540.0540.0430.0430.0940.0280.0510.1160.0170.0760.0690.0770.0730.0780.0420.0420.0000.0430.0470.0140.0930.0940.0040.0830.0190.0350.0810.0090.0900.0890.0091.0000.0950.0360.0370.0540.0370.0590.2630.0750.0680.0670.0311.0000.0150.0840.1220.0530.0770.1170.0850.0810.0680.0630.0590.0690.0790.0700.0700.0660.0700.0570.0690.0780.0720.0700.0670.071
FAMI_SITUACIONECONOMICA0.0720.0030.0080.0190.0240.0360.0370.0320.0320.0580.0140.0220.0300.0130.0640.0420.0730.0620.0690.0320.0320.0000.0320.0360.0360.0580.0580.0030.0250.0480.0430.0650.0070.0410.0590.0071.0000.0180.0130.0560.0870.0710.0830.0530.0360.0370.0510.0270.0151.0000.0710.0650.1000.0900.0440.0520.0980.0780.0550.0700.0710.0830.0500.0790.0690.0750.0110.0670.0780.0460.0770.0660.072
FAMI_TIENEAUTOMOVIL0.0730.0810.1760.1080.1220.2660.2650.0740.0740.1760.0990.2560.3170.0470.1990.2790.1760.1900.1870.0710.0710.0000.0700.1590.0360.1780.1780.0170.2360.0500.0670.5270.0350.3520.4930.0351.0000.0680.0800.0490.2220.1720.2400.1580.3290.3170.3660.2330.0840.0711.0000.3050.3090.2920.2550.2000.0020.1860.2800.3160.1870.2090.2360.1670.1930.1760.1620.2080.2310.2800.1850.2100.194
FAMI_TIENECOMPUTADOR0.2260.0280.1080.0850.1320.2580.2590.1820.1820.3020.0970.1950.2750.0540.2610.3050.2620.2590.2670.1820.1820.0000.1820.3230.0550.3030.3040.0080.3150.0370.1610.6900.0350.4410.6680.0351.0000.1210.1140.1020.2790.2050.3150.1630.3670.3330.3760.2900.1220.0650.3051.0000.2810.3280.5490.3060.0020.2790.2850.3210.2700.3110.3070.2740.2730.2730.1010.2730.3100.3140.2740.2760.276
FAMI_TIENECONSOLAVIDEOJUEGOS0.0950.0680.1450.1130.0920.2170.2170.1610.1610.2340.0760.1960.2630.0380.1460.2320.1310.1450.1410.1640.1640.0000.1630.1830.0660.2390.2380.0100.2200.1740.0640.4740.0220.3110.4460.0221.0000.0700.0410.0360.2130.1650.2390.1380.2610.2620.3330.1940.0530.1000.3090.2811.0000.2970.2510.1850.0100.1820.2260.2650.1360.1570.1970.1240.1450.1310.1350.1540.1750.2330.1370.1600.147
FAMI_TIENEHORNOMICROOGAS0.1240.0420.1210.0970.0960.2140.2140.1790.1800.2290.0800.1830.2560.0400.1550.2320.1490.1460.1520.1810.1820.0000.1810.2350.0550.2320.2320.0110.2460.0880.0980.5250.0160.3290.5040.0161.0000.0840.0660.0510.2690.1930.2890.1510.2890.2750.3370.2220.0770.0900.2920.3280.2971.0000.3160.2510.0020.2470.2330.2620.1510.1740.2110.1470.1500.1470.1120.1620.1840.2340.1570.1600.158
FAMI_TIENEINTERNET0.3000.0120.0940.0960.1420.2920.2950.2190.2180.3270.0860.1400.2610.0500.2480.2840.2600.2520.2590.2210.2200.0000.2190.4270.0620.3280.3290.0070.3140.0380.1730.7070.0150.4620.6750.0151.0000.1660.0930.1090.2770.1880.3000.1600.3530.3270.3850.2370.1170.0440.2550.5490.2510.3161.0000.3420.0140.3230.2850.3560.2580.3050.2950.2740.2650.2660.0910.2600.3020.2980.2730.2660.267
FAMI_TIENELAVADORA0.1640.0000.0660.0880.0780.1700.1720.1920.1920.2580.0580.1030.1710.0420.1410.1720.1460.1440.1380.1930.1930.0000.1920.2520.0470.2590.2590.0030.1870.0460.1150.5130.0410.3070.4920.0411.0000.1270.0950.0750.2440.1320.2550.1460.2590.2340.2590.1640.0850.0520.2000.3060.1850.2510.3421.0000.0400.2690.2120.2450.1450.1690.1750.1520.1510.1410.0610.1470.1690.1790.1530.1530.143
FAMI_TIENEMOTOCICLETA0.1070.0180.0480.0890.0550.1290.1320.2220.2220.2640.0520.0980.1130.0160.0890.1240.0940.0800.1080.2240.2240.0010.2230.0350.0340.2660.2660.0030.0910.0390.0820.0840.0390.1630.0740.0391.0000.0270.0450.0820.0370.0190.0410.0780.0770.1040.0950.0590.0810.0980.0020.0020.0100.0020.0140.0401.0000.0290.0830.1180.0870.1070.1130.0960.0810.1070.0490.0930.1110.1250.0990.0870.111
FAMI_TIENESERVICIOTV0.1310.0150.0670.0660.0800.1530.1530.1380.1370.1830.0550.1160.1590.0330.1130.1490.1160.1130.1090.1380.1370.0000.1380.2350.0380.1830.1840.0060.1690.0060.1070.4580.0170.2540.4390.0171.0000.0690.0790.0710.2370.1410.2350.1370.2200.2060.2460.1390.0680.0780.1860.2790.1820.2470.3230.2690.0291.0000.1720.2060.1150.1340.1490.1200.1160.1100.0630.1180.1370.1560.1230.1190.113
FAMI_TRABAJOLABORMADRE0.2110.0920.1460.0660.0480.1070.1080.0570.0570.0760.0780.1060.2890.0570.1300.1500.1270.1230.1320.0570.0570.0000.0570.1020.0340.0760.0760.0300.1320.0600.0790.2290.0140.2340.3620.0141.0000.0800.0290.0890.1120.0840.1260.0430.2230.1310.1440.1440.0630.0550.2800.2850.2260.2330.2850.2120.0830.1721.0000.2180.0740.0850.0880.0730.0730.0750.2400.0840.0900.1080.0820.0780.080
FAMI_TRABAJOLABORPADRE0.3140.1160.1930.0770.0560.1320.1330.0680.0680.0810.0810.1110.3180.0610.1450.1720.1400.1370.1440.0680.0680.0000.0680.1270.0380.0820.0820.0260.1540.0530.0930.2510.0160.2780.4060.0161.0000.1070.0300.1000.1310.0950.1410.0510.1480.2490.1680.1510.0590.0700.3160.3210.2650.2620.3560.2450.1180.2060.2181.0000.0810.0940.1000.0800.0810.0800.3160.0940.1010.1240.0910.0870.087
PERCENTIL_C_NATURALES0.1470.0290.0360.046-0.1250.0850.083-0.002-0.0060.0830.0890.1410.2200.0530.7700.3330.4350.4560.491-0.003-0.007-0.004-0.0080.1140.0700.0830.0840.0000.2010.0890.0980.3760.0050.2440.2280.0050.0130.1490.0720.0790.1120.0860.1350.0470.1170.1110.1030.1620.0690.0710.1870.2700.1360.1510.2580.1450.0870.1150.0740.0811.0000.8890.6280.7080.7310.7570.0000.9920.8820.6170.7030.7270.754
PERCENTIL_GLOBAL0.1910.0390.0390.057-0.1370.0800.077-0.029-0.0340.0930.1020.1570.2500.0610.6110.3860.6110.5980.642-0.031-0.035-0.031-0.0360.1330.0810.0930.0940.0030.2180.0750.1110.4330.0050.2840.2610.0050.0150.1820.0780.0860.1240.0980.1540.0510.1330.1250.1240.1810.0790.0830.2090.3110.1570.1740.3050.1690.1070.1340.0850.0940.8891.0000.7030.8870.8770.9090.0000.8830.9910.6900.8810.8720.904
PERCENTIL_INGLES0.1700.0570.0400.067-0.1320.0970.095-0.046-0.0520.0800.1030.1510.2920.0600.3900.6720.3480.3420.378-0.049-0.054-0.049-0.0550.1230.0630.0810.0810.0040.1970.0510.1070.4340.0080.3010.2770.0080.0140.1350.0640.0930.1200.0910.1440.0450.1340.1300.1350.1730.0700.0500.2360.3070.1970.2110.2950.1750.1130.1490.0880.1000.6280.7031.0000.5800.5770.5960.0000.6240.6980.9800.5770.5740.594
PERCENTIL_LECTURA_CRITICA0.1820.0270.0330.048-0.1190.0600.058-0.037-0.0420.0780.0880.1260.2070.0530.4390.3020.8510.4230.505-0.038-0.043-0.039-0.0440.1190.0810.0780.0790.0050.1980.0240.1030.3830.0060.2390.2210.0060.0160.1640.0660.0770.1030.0860.1310.0420.1130.1060.1060.1560.0700.0790.1670.2740.1240.1470.2740.1520.0960.1200.0730.0800.7080.8870.5801.0000.7070.7900.0000.7030.8800.5700.9920.7030.786
PERCENTIL_MATEMATICAS0.1670.0340.0370.046-0.1240.0710.069-0.018-0.0220.0840.0870.1410.2090.0560.4670.3020.4270.8460.440-0.019-0.023-0.020-0.0240.1150.0560.0840.0850.0020.1990.1290.0850.3820.0060.2440.2260.0060.0120.1550.0740.0620.1140.0850.1370.0460.1160.1080.1030.1570.0660.0690.1930.2730.1450.1500.2650.1510.0810.1160.0730.0810.7310.8770.5770.7071.0000.7020.0000.7260.8700.5670.7020.9930.699
PERCENTIL_SOCIALES_CIUDADANAS0.1670.0300.0300.049-0.1210.0690.067-0.039-0.0440.0780.0920.1280.2220.0520.4780.3200.4890.4210.836-0.041-0.046-0.042-0.0460.1150.0860.0790.0790.0040.2040.0510.1030.3770.0050.2460.2250.0050.0110.1480.0610.0770.1000.0890.1320.0420.1150.1080.1050.1630.0700.0750.1760.2730.1310.1470.2660.1410.1070.1100.0750.0800.7570.9090.5960.7900.7021.0000.0000.7520.9010.5850.7840.6980.995
PERIODO0.0180.2800.8640.1010.1040.3080.3070.1810.1810.2380.0610.1760.2990.0380.1450.3360.1360.1250.1180.1810.1810.0000.1790.0640.0240.2390.2360.1050.1230.0190.0391.0000.0341.0001.0000.0340.0000.0370.0270.0230.0940.0980.0980.0440.2140.2270.3360.1370.0570.0110.1620.1010.1350.1120.0910.0610.0490.0630.2400.3160.0000.0000.0000.0000.0000.0001.0000.1600.1820.3410.1610.1400.148
PUNT_C_NATURALES0.1440.1070.1350.053-0.1350.1120.1100.0070.0030.0860.0960.1450.2560.0560.9260.3640.4380.4730.5000.0050.0010.004-0.0000.1130.0690.0870.0870.0220.2720.0870.0970.3760.0060.2430.2240.0060.0120.1470.0730.0780.1150.0900.1380.0470.1330.1290.1180.1690.0690.0670.2080.2730.1540.1620.2600.1470.0930.1180.0840.0940.9920.8830.6240.7030.7260.7520.1601.0000.8910.6340.7110.7350.760
PUNT_GLOBAL0.1860.1280.1520.064-0.1490.1090.106-0.018-0.0240.0900.1090.1610.2890.0640.6560.4290.5870.5990.642-0.021-0.026-0.021-0.0270.1310.0790.0910.0910.0280.3910.0730.1090.4330.0090.2800.2560.0090.0140.1750.0750.0860.1270.1020.1550.0500.1420.1370.1410.1880.0780.0780.2310.3100.1750.1840.3020.1690.1110.1370.0900.1010.8820.9910.6980.8800.8700.9010.1820.8911.0000.7090.8890.8790.909
PUNT_INGLES0.1670.2520.2700.082-0.1470.1400.138-0.027-0.0330.0830.1150.1690.3620.0670.4140.7720.3610.3570.394-0.030-0.036-0.031-0.0370.1240.0620.0860.0860.0380.2070.0550.1070.4330.0190.3090.2790.0190.0130.1280.0660.0920.1290.1020.1530.0470.1640.1640.1880.1910.0720.0460.2800.3140.2330.2340.2980.1790.1250.1560.1080.1240.6170.6900.9800.5700.5670.5850.3410.6340.7091.0000.5870.5840.600
PUNT_LECTURA_CRITICA0.1790.0990.1330.053-0.1290.0860.084-0.027-0.0330.0820.0920.1300.2390.0560.4470.3250.7890.4260.500-0.030-0.035-0.030-0.0360.1180.0780.0820.0830.0320.2310.0230.1020.3830.0090.2330.2150.0090.0130.1630.0680.0770.1060.0890.1330.0430.1260.1210.1180.1600.0700.0770.1850.2740.1370.1570.2730.1530.0990.1230.0820.0910.7030.8810.5770.9920.7020.7840.1610.7110.8890.5871.0000.7110.792
PUNT_MATEMATICAS0.1640.1010.1200.051-0.1340.0960.094-0.008-0.0130.0830.0940.1450.2420.0590.4960.3270.4320.9130.450-0.011-0.015-0.011-0.0160.1150.0550.0830.0840.0210.2580.1280.0850.3820.0070.2400.2220.0070.0110.1540.0730.0640.1170.0890.1390.0460.1230.1170.1160.1620.0670.0660.2100.2760.1600.1600.2660.1530.0870.1190.0780.0870.7270.8720.5740.7030.9930.6980.1400.7350.8790.5840.7111.0000.705
PUNT_SOCIALES_CIUDADANAS0.1640.1010.1240.055-0.1300.0910.089-0.032-0.0370.0780.0970.1330.2520.0550.4980.3480.4910.4320.933-0.034-0.039-0.035-0.0400.1150.0840.0780.0790.0240.2520.0520.1030.3770.0010.2460.2240.0010.0110.1450.0590.0770.1050.0930.1350.0420.1230.1180.1190.1700.0710.0720.1940.2760.1470.1580.2670.1430.1110.1130.0800.0870.7540.9040.5940.7860.6990.9950.1480.7600.9090.6000.7920.7051.000

Missing values

2024-10-24T11:44:49.003535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-24T11:44:51.161602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-24T11:44:57.291735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ESTU_TIPODOCUMENTOESTU_NACIONALIDADESTU_GENEROESTU_FECHANACIMIENTOPERIODOESTU_CONSECUTIVOESTU_ESTUDIANTEESTU_PAIS_RESIDEESTU_TIENEETNIAESTU_DEPTO_RESIDEESTU_COD_RESIDE_DEPTOESTU_MCPIO_RESIDEESTU_COD_RESIDE_MCPIOFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_CUARTOSHOGARFAMI_EDUCACIONPADREFAMI_EDUCACIONMADREFAMI_TRABAJOLABORPADREFAMI_TRABAJOLABORMADREFAMI_TIENEINTERNETFAMI_TIENESERVICIOTVFAMI_TIENECOMPUTADORFAMI_TIENELAVADORAFAMI_TIENEHORNOMICROOGASFAMI_TIENEAUTOMOVILFAMI_TIENEMOTOCICLETAFAMI_TIENECONSOLAVIDEOJUEGOSFAMI_NUMLIBROSFAMI_COMELECHEDERIVADOSFAMI_COMECARNEPESCADOHUEVOFAMI_COMECEREALFRUTOSLEGUMBREFAMI_SITUACIONECONOMICAESTU_DEDICACIONLECTURADIARIAESTU_DEDICACIONINTERNETESTU_HORASSEMANATRABAJAESTU_TIPOREMUNERACIONCOLE_CODIGO_ICFESCOLE_COD_DANE_ESTABLECIMIENTOCOLE_NOMBRE_ESTABLECIMIENTOCOLE_GENEROCOLE_NATURALEZACOLE_CALENDARIOCOLE_BILINGUECOLE_CARACTERCOLE_COD_DANE_SEDECOLE_NOMBRE_SEDECOLE_SEDE_PRINCIPALCOLE_AREA_UBICACIONCOLE_JORNADACOLE_COD_MCPIO_UBICACIONCOLE_MCPIO_UBICACIONCOLE_COD_DEPTO_UBICACIONCOLE_DEPTO_UBICACIONESTU_PRIVADO_LIBERTADESTU_COD_MCPIO_PRESENTACIONESTU_MCPIO_PRESENTACIONESTU_DEPTO_PRESENTACIONESTU_COD_DEPTO_PRESENTACIONPUNT_LECTURA_CRITICAPERCENTIL_LECTURA_CRITICADESEMP_LECTURA_CRITICAPUNT_MATEMATICASPERCENTIL_MATEMATICASDESEMP_MATEMATICASPUNT_C_NATURALESPERCENTIL_C_NATURALESDESEMP_C_NATURALESPUNT_SOCIALES_CIUDADANASPERCENTIL_SOCIALES_CIUDADANASDESEMP_SOCIALES_CIUDADANASPUNT_INGLESPERCENTIL_INGLESDESEMP_INGLESPUNT_GLOBALPERCENTIL_GLOBALESTU_ESTADOINVESTIGACIONESTU_GENERACION-EESTU_INSE_INDIVIDUALESTU_NSE_INDIVIDUALESTU_NSE_ESTABLECIMIENTO
0CCCOLOMBIAF01/01/198520201SB11202010045555ESTUDIANTECOLOMBIANoCESAR20.0SAN DIEGO20750.0Estrato 15 a 6UnoPrimaria incompletaPrimaria incompletaTrabaja en el hogar, no trabaja o estudiaTrabaja por cuenta propia (por ejemplo plomero, electricista)NoNoNoNoNoNoNoNo0 A 10 LIBROS1 o 2 veces por semana1 o 2 veces por semanaTodos o casi todos los díasMejorNo leo por entretenimientoNo Navega InternetMás de 30 horasSi, en efectivo57372120750000415I.E. MANUEL RODRIGUEZ TORICESMIXTOOFICIALASTÉCNICO/ACADÉMICO120750000415I.E. MANUEL RODRIGUEZ TORICESSURBANONOCHE20750SAN DIEGO20CESARN20001.0VALLEDUPARCESAR20.0396232413561241136.07A-1642.0VALIDEZ OFICINA JURÍDICAGENERACION E - GRATUIDADNaNNaNNaN
1CCCOLOMBIAF01/01/199520201SB11202010045719ESTUDIANTECOLOMBIANoNARIÑO52.0IPIALES52356.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN177618352356001951INSTITUTO DE EDUCACIÓN TECNICA INESURMIXTONO OFICIALANaNNaN352356001951INSTITUTO DE EDUCACIÓN TECNICA INESUR - SEDE PRINCIPALSURBANOSABATINA52356IPIALES52NARIÑON52356.0IPIALESNARIÑO52.0417241122391014423230.03A-20210.0PUBLICARGENERACION E - GRATUIDADNaNNaNNaN
2CCCOLOMBIAF01/01/199720201SB11202010070662ESTUDIANTECOLOMBIASiCAUCA19.0TOTORÓ19824.0Estrato 13 a 4CuatroSecundaria (Bachillerato) incompletaSecundaria (Bachillerato) incompletaTrabaja en el hogar, no trabaja o estudiaTrabaja en el hogar, no trabaja o estudiaNoNoNoNoNoNoNoNo0 A 10 LIBROS1 o 2 veces por semanaNaNNaNIgual30 minutos o menosNo Navega InternetEntre 11 y 20 horasSi, en efectivo135301319001004669COORPORACION EDUCATIVA DEL SUR OCCIDENTE COLOMBIANOMIXTONO OFICIALOTRONaNACADÉMICO319001004669COORPORACION EDUCATIVA DEL SUR OCCIDENTE COLOMBIANO - SEDE PRINCIPALSURBANOMAÑANA19001POPAYÁN19CAUCAN19001.0POPAYÁNCAUCA19.0374236723341241130.03A-1622.0PUBLICARGENERACION E - GRATUIDADNaNNaNNaN
3CCCOLOMBIAF01/01/200120201SB11202010069926ESTUDIANTECOLOMBIANoPUTUMAYO86.0MOCOA86001.0Estrato 11 a 2UnoTécnica o tecnológica completaEducación profesional completaEs dueño de un negocio grande, tiene un cargo de nivel directivo o gerencialTrabaja como profesional (por ejemplo médico, abogado, ingeniero)SiNoSiSiNoNoSiNo11 A 25 LIBROS1 o 2 veces por semana3 a 5 veces por semana1 o 2 veces por semanaIgual30 minutos o menosEntre 1 y 3 horasEntre 11 y 20 horasSi, en efectivo155739386001003939NUEVO INSTITUTO DE APRENDIZAJE SURCOLOMBIANOMIXTONO OFICIALANaNTÉCNICO/ACADÉMICO386001003939NUEVO INSTITUTO DE APRENDIZAJE SURCOLOMBIANO - SEDE PRINCIPALSURBANOSABATINA86001MOCOA86PUTUMAYON86001.0MOCOAPUTUMAYO86.038524213238101337137.08A-1886.0PUBLICARNONaNNaNNaN
4CCCOLOMBIAF02/01/200120201SB11202010023181ESTUDIANTECOLOMBIANoRISARALDA66.0PEREIRA66001.0Estrato 63 a 4CuatroEducación profesional completaSecundaria (Bachillerato) completaTrabaja como profesional (por ejemplo médico, abogado, ingeniero)Trabaja en el hogar, no trabaja o estudiaSiSiSiSiSiSiNoNo26 A 100 LIBROSTodos o casi todos los díasTodos o casi todos los días3 a 5 veces por semanaIgualNo leo por entretenimientoMás de 3 horas0No77776366001003814COL FUNDACION LIC INGLESMIXTONO OFICIALBSACADÉMICO366001003814COL FUNDACION LIC INGLESSRURALCOMPLETA66001PEREIRA66RISARALDAN66001.0PEREIRARISARALDA66.05842350252503225342280.080B+27439.0PUBLICARNONaNNaNNaN
5CCCOLOMBIAF02/01/200120201SB11202010057992ESTUDIANTECOLOMBIANoANTIOQUIA5.0MACEO5425.0Estrato 17 a 8CuatroPrimaria completaPrimaria incompletaTrabaja como personal de limpieza, mantenimiento, seguridad o construcciónTrabaja en el hogar, no trabaja o estudiaSiNoSiSiSiNoNoNo0 A 10 LIBROS1 o 2 veces por semana3 a 5 veces por semana1 o 2 veces por semanaPeorEntre 30 y 60 minutosMás de 3 horas0No662858305001022461COL GENTE UNIDA JOVENES POR LA PAZ - LUZ DE ORIENTEMIXTONO OFICIALANaNTÉCNICO/ACADÉMICO305001022461COL GENTE UNIDA JOVENES POR LA PAZ - LUZ DE ORIENTESURBANOSABATINA5001MEDELLÍN5ANTIOQUIAN5001.0MEDELLÍNANTIOQUIA5.0481824416244202262129.02A-1989.0PUBLICARGENERACION E - GRATUIDADNaNNaNNaN
6CCCOLOMBIAF03/01/200120201SB11202010074718ESTUDIANTECOLOMBIANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN177618352356001951INSTITUTO DE EDUCACIÓN TECNICA INESURMIXTONO OFICIALANaNNaN352356001951INSTITUTO DE EDUCACIÓN TECNICA INESUR - SEDE PRINCIPALSURBANOSABATINA52356IPIALES52NARIÑON52356.0IPIALESNARIÑO52.03221391022711293136.07A-1602.0PUBLICARGENERACION E - GRATUIDADNaNNaNNaN
7CCCOLOMBIAF04/01/198220201SB11202010070513ESTUDIANTECOLOMBIANoANTIOQUIA5.0MEDELLÍN5001.0Estrato 13 a 4DosNingunoPrimaria incompletaTrabaja en el hogar, no trabaja o estudiaNaNNoNoNoNoNoNoNoNo0 A 10 LIBROS1 o 2 veces por semanaNunca o rara vez comemos esoNunca o rara vez comemos esoIgual30 minutos o menosNo Navega InternetMás de 30 horasSi, en efectivo95869305001022682POLITÉCNICO MAYOR AGENCIA CRISTIANA DE SERVICIO Y EDUCACIÓNMIXTONO OFICIALOTRONACADÉMICO305001022682POLITÉCNICO MAYOR AGENCIA CRISTIANA DE SERVICIO Y EDUCACIÓNSURBANOMAÑANA5001MEDELLÍN5ANTIOQUIAN5001.0MEDELLÍNANTIOQUIA5.0451323672442024118239.010A-20711.0PUBLICARGENERACION E - GRATUIDADNaNNaNNaN
8CCCOLOMBIAF04/01/198920201SB11202010067334ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 55 a 6TresNingunoPrimaria incompletaTrabaja en el hogar, no trabaja o estudiaTrabaja en el hogar, no trabaja o estudiaSiNoSiSiNoNoNoNo26 A 100 LIBROS3 a 5 veces por semana1 o 2 veces por semanaTodos o casi todos los díasPeorEntre 30 y 60 minutosEntre 30 y 60 minutosMás de 30 horasSi, en efectivo88575311001047723COL CENT DE PROMOCION SAN JOSEMIXTONO OFICIALANACADÉMICO311001047723COL CENT DE PROMOCION SAN JOSESURBANOSABATINA11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.03531292128113915129.02A-1622.0PUBLICARNONaNNaNNaN
9CCCOLOMBIAF04/01/199920201SB11202010069900ESTUDIANTECOLOMBIANoMETA50.0ACACÍAS50006.0Estrato 25 a 6TresNingunoPrimaria incompletaEs vendedor o trabaja en atención al públicoTrabaja en el hogar, no trabaja o estudiaSiSiSiSiSiNoNoNo0 A 10 LIBROS1 o 2 veces por semana3 a 5 veces por semanaTodos o casi todos los díasIgual30 minutos o menosMás de 3 horasEntre 21 y 30 horasSi, en efectivo127944350001006933GIMNASIO INTERACTIVO KAIZENMIXTONO OFICIALANaNACADÉMICO350001006933GIMNASIO INTERACTIVO KAIZEN - SEDE PRINCIPALSURBANOSABATINA50001VILLAVICENCIO50METAN50001.0VILLAVICENCIOMETA50.0532933241421524525233.05A-21113.0PUBLICARNONaNNaNNaN
ESTU_TIPODOCUMENTOESTU_NACIONALIDADESTU_GENEROESTU_FECHANACIMIENTOPERIODOESTU_CONSECUTIVOESTU_ESTUDIANTEESTU_PAIS_RESIDEESTU_TIENEETNIAESTU_DEPTO_RESIDEESTU_COD_RESIDE_DEPTOESTU_MCPIO_RESIDEESTU_COD_RESIDE_MCPIOFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_CUARTOSHOGARFAMI_EDUCACIONPADREFAMI_EDUCACIONMADREFAMI_TRABAJOLABORPADREFAMI_TRABAJOLABORMADREFAMI_TIENEINTERNETFAMI_TIENESERVICIOTVFAMI_TIENECOMPUTADORFAMI_TIENELAVADORAFAMI_TIENEHORNOMICROOGASFAMI_TIENEAUTOMOVILFAMI_TIENEMOTOCICLETAFAMI_TIENECONSOLAVIDEOJUEGOSFAMI_NUMLIBROSFAMI_COMELECHEDERIVADOSFAMI_COMECARNEPESCADOHUEVOFAMI_COMECEREALFRUTOSLEGUMBREFAMI_SITUACIONECONOMICAESTU_DEDICACIONLECTURADIARIAESTU_DEDICACIONINTERNETESTU_HORASSEMANATRABAJAESTU_TIPOREMUNERACIONCOLE_CODIGO_ICFESCOLE_COD_DANE_ESTABLECIMIENTOCOLE_NOMBRE_ESTABLECIMIENTOCOLE_GENEROCOLE_NATURALEZACOLE_CALENDARIOCOLE_BILINGUECOLE_CARACTERCOLE_COD_DANE_SEDECOLE_NOMBRE_SEDECOLE_SEDE_PRINCIPALCOLE_AREA_UBICACIONCOLE_JORNADACOLE_COD_MCPIO_UBICACIONCOLE_MCPIO_UBICACIONCOLE_COD_DEPTO_UBICACIONCOLE_DEPTO_UBICACIONESTU_PRIVADO_LIBERTADESTU_COD_MCPIO_PRESENTACIONESTU_MCPIO_PRESENTACIONESTU_DEPTO_PRESENTACIONESTU_COD_DEPTO_PRESENTACIONPUNT_LECTURA_CRITICAPERCENTIL_LECTURA_CRITICADESEMP_LECTURA_CRITICAPUNT_MATEMATICASPERCENTIL_MATEMATICASDESEMP_MATEMATICASPUNT_C_NATURALESPERCENTIL_C_NATURALESDESEMP_C_NATURALESPUNT_SOCIALES_CIUDADANASPERCENTIL_SOCIALES_CIUDADANASDESEMP_SOCIALES_CIUDADANASPUNT_INGLESPERCENTIL_INGLESDESEMP_INGLESPUNT_GLOBALPERCENTIL_GLOBALESTU_ESTADOINVESTIGACIONESTU_GENERACION-EESTU_INSE_INDIVIDUALESTU_NSE_INDIVIDUALESTU_NSE_ESTABLECIMIENTO
520297TICOLOMBIAM02/26/2003 12:00:00 AM20204SB11202040115228ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 35 a 6CincoSecundaria (Bachillerato) completaTécnica o tecnológica incompletaTiene un trabajo de tipo auxiliar administrativo (por ejemplo, secretario o asistente)Tiene un trabajo de tipo auxiliar administrativo (por ejemplo, secretario o asistente)SiSiSiSiSiNoNoSiMÁS DE 100 LIBROS3 a 5 veces por semana3 a 5 veces por semana3 a 5 veces por semanaMejor30 minutos o menosEntre 1 y 3 horasMenos de 10 horasNo91314311001097569LIC LATINOAMERICANO DEL SURMIXTONO OFICIALANACADÉMICO311001097569LIC LATINOAMERICANO DEL SUR - SEDE PRINCIPALSURBANOCOMPLETA11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.05144355643526524956262.089A226363.0PUBLICARNO59.2937533.03.0
520298TICOLOMBIAM06/11/2003 12:00:00 AM20204SB11202040112449ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 33 a 4TresSecundaria (Bachillerato) incompletaSecundaria (Bachillerato) completaEs dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etcEs dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etcSiNoSiNoSiNoSiNo0 A 10 LIBROS1 o 2 veces por semanaTodos o casi todos los días1 o 2 veces por semanaIgual30 minutos o menosEntre 1 y 3 horas0No91314311001097569LIC LATINOAMERICANO DEL SURMIXTONO OFICIALANACADÉMICO311001097569LIC LATINOAMERICANO DEL SUR - SEDE PRINCIPALSURBANOCOMPLETA11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.05973366903454125057244.046A-27168.0PUBLICARNO51.3402903.03.0
520299CCCOLOMBIAM11/08/1989 12:00:00 AM20204SB11202040547511ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 35 a 6CuatroTécnica o tecnológica incompletaSecundaria (Bachillerato) incompletaTrabaja por cuenta propia (por ejemplo plomero, electricista)Trabaja como personal de limpieza, mantenimiento, seguridad o construcciónSiSiSiSiSiNoSiSi0 A 10 LIBROS3 a 5 veces por semana3 a 5 veces por semana1 o 2 veces por semanaIgualNo leo por entretenimientoEntre 1 y 3 horasEntre 11 y 20 horasSi, en efectivo729905311001800405INSTITUCION EDUCATIVA FRAY LUIS DE LEONMIXTONO OFICIALANaNNaN311001800405INSTITUCION EDUCATIVA FRAY LUIS DE LEON - SEDE PRINCIPALSURBANOSABATINA11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.0473222931351013719134.010A-1849.0PUBLICARGENERACION E - GRATUIDAD52.4096403.03.0
520300TICOLOMBIAM07/23/2002 12:00:00 AM20204SB11202040434267ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 37 a 8Seis o masSecundaria (Bachillerato) incompletaEducación profesional completaTrabaja por cuenta propia (por ejemplo plomero, electricista)Trabaja como profesional (por ejemplo médico, abogado, ingeniero)SiSiSiSiSiSiSiSiMÁS DE 100 LIBROSTodos o casi todos los díasTodos o casi todos los días3 a 5 veces por semanaMejorNo leo por entretenimiento30 minutos o menos0No722546125473000242I.E. LA MERCEDMIXTOOFICIALANACADÉMICO125473000242I.E. LA MERCED - SEDE PRINCIPALSURBANOUNICA25473MOSQUERA25CUNDINAMARCAN11001.0BOGOTÁ D.C.BOGOTÁ11.05766361813629035364261.089A229281.0PUBLICARNO67.9479354.03.0
520301CCCOLOMBIAF04/05/2002 12:00:00 AM20204SB11202040115424ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 23 a 4CincoTécnica o tecnológica incompletaEducación profesional incompletaTrabaja como profesional (por ejemplo médico, abogado, ingeniero)Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etcSiSiSiSiNoSiSiSi26 A 100 LIBROSNunca o rara vez comemos eso1 o 2 veces por semana1 o 2 veces por semanaPeorEntre 1 y 2 horasMás de 3 horas0No91314311001097569LIC LATINOAMERICANO DEL SURMIXTONO OFICIALANACADÉMICO311001097569LIC LATINOAMERICANO DEL SUR - SEDE PRINCIPALSURBANOCOMPLETA11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.05561348392392114338244.047A-23039.0PUBLICARNO61.4576113.03.0
520302TICOLOMBIAM12/26/2003 12:00:00 AM20204SB11202040105446ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 25 a 6TresTécnica o tecnológica completaSecundaria (Bachillerato) incompletaEs operario de máquinas o conduce vehículos (taxita, chofer)Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etcSiNoNoSiNoNoSiNo0 A 10 LIBROS1 o 2 veces por semana1 o 2 veces por semanaNunca o rara vez comemos esoPeor30 minutos o menosMás de 3 horasMenos de 10 horasNo85548311001088331GIMN FELIX (GIMN SABER DEL NILO)MIXTONO OFICIALANACADÉMICO311001088331GIMN FELIX (GIMN SABER DEL NILO)SURBANOCOMPLETA11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.06076358733608735159245.052A-28275.0PUBLICARNO45.3114492.02.0
520303CCCOLOMBIAM10/27/1996 12:00:00 AM20204SB11202040168415ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 23 a 4DosPrimaria incompletaSecundaria (Bachillerato) incompletaEs vendedor o trabaja en atención al públicoTrabaja en el hogar, no trabaja o estudiaNoNoNoSiNoNoNoNo11 A 25 LIBROSNunca o rara vez comemos eso1 o 2 veces por semanaNunca o rara vez comemos esoIgual30 minutos o menosEntre 30 y 60 minutosMenos de 10 horasSi, en efectivo143578111769003416COLEGIO JUAN LOZANO Y LOZANO (IED)MIXTOOFICIALANACADÉMICO111769003416COL DIST JUAN LOZANO Y LOZANOSURBANONOCHE11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.05767355623402415366254.079A125759.0PUBLICARNO38.1247291.03.0
520304TICOLOMBIAF01/14/2005 12:00:00 AM20204SB11202040185843ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 25 a 6CuatroPrimaria incompletaSecundaria (Bachillerato) completaEs operario de máquinas o conduce vehículos (taxita, chofer)Trabaja por cuenta propia (por ejemplo plomero, electricista)SiSiSiSiSiSiNoNo0 A 10 LIBROS3 a 5 veces por semanaTodos o casi todos los días3 a 5 veces por semanaIgual30 minutos o menosEntre 1 y 3 horasMenos de 10 horasSi, en efectivo46698111769003416COLEGIO JUAN LOZANO Y LOZANO (IED)MIXTOOFICIALANACADÉMICO111769003416COL DIST JUAN LOZANO Y LOZANOSURBANOTARDE11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.078100459763629036591367.093A233095.0PUBLICARNO53.5864433.03.0
520305TICOLOMBIAM06/14/2002 12:00:00 AM20204SB11202040168607ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 23 a 4TresSecundaria (Bachillerato) incompletaPrimaria completaEs operario de máquinas o conduce vehículos (taxita, chofer)Trabaja en el hogar, no trabaja o estudiaSiSiSiSiSiSiNoSi26 A 100 LIBROSTodos o casi todos los díasTodos o casi todos los días3 a 5 veces por semanaMejorEntre 30 y 60 minutosEntre 1 y 3 horas0No143578111769003416COLEGIO JUAN LOZANO Y LOZANO (IED)MIXTOOFICIALANACADÉMICO111769003416COL DIST JUAN LOZANO Y LOZANOSURBANONOCHE11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.06996457693526625672352.075A129080.0PUBLICARNO58.3169363.03.0
520306TICOLOMBIAF02/20/2002 12:00:00 AM20204SB11202040525571ESTUDIANTECOLOMBIANoBOGOTÁ11.0BOGOTÁ D.C.11001.0Estrato 33 a 4DosSecundaria (Bachillerato) completaSecundaria (Bachillerato) completaEs operario de máquinas o conduce vehículos (taxita, chofer)Es dueño de un negocio pequeño (tiene pocos empleados o no tiene, por ejemplo tienda, papelería, etcSiSiSiSiSiNoNoNo26 A 100 LIBROSTodos o casi todos los díasTodos o casi todos los díasNunca o rara vez comemos esoIgual30 minutos o menos30 minutos o menos0No46698111769003416COLEGIO JUAN LOZANO Y LOZANO (IED)MIXTOOFICIALANACADÉMICO111769003416COL DIST JUAN LOZANO Y LOZANOSURBANOTARDE11001BOGOTÁ D.C.11BOGOTÁN11001.0BOGOTÁ D.C.BOGOTÁ11.05870351513495525263248.063A126161.0PUBLICARGENERACION E - GRATUIDAD57.3757303.03.0